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vishnupriya1234 · 3 months ago

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The Essential Tools Every Data Analyst Must Know

The role of a data analyst requires a strong command of various tools and technologies to efficiently collect, clean, analyze, and visualize data. These tools help transform raw data into actionable insights that drive business decisions. Whether you’re just starting your journey as a data analyst or looking to refine your skills, understanding the essential tools will give you a competitive edge in the field from the best Data Analytics Online Training.

SQL – The Backbone of Data Analysis

Structured Query Language (SQL) is one of the most fundamental tools for data analysts. It allows professionals to interact with databases, extract relevant data, and manipulate large datasets efficiently. Since most organizations store their data in relational databases like MySQL, PostgreSQL, and Microsoft SQL Server, proficiency in SQL is a must. Analysts use SQL to filter, aggregate, and join datasets, making it easier to conduct in-depth analysis.

Excel – The Classic Data Analysis Tool

Microsoft Excel remains a powerful tool for data analysis, despite the rise of more advanced technologies. With its built-in formulas, pivot tables, and data visualization features, Excel is widely used for quick data manipulation and reporting. Analysts often use Excel for smaller datasets and preliminary data exploration before transitioning to more complex tools. If you want to learn more about Data Analytics, consider enrolling in an Best Online Training & Placement programs . They often offer certifications, mentorship, and job placement opportunities to support your learning journey.

Python and R – The Power of Programming

Python and R are two of the most commonly used programming languages in data analytics. Python, with libraries like Pandas, NumPy, and Matplotlib, is excellent for data manipulation, statistical analysis, and visualization. R is preferred for statistical computing and machine learning tasks, offering packages like ggplot2 and dplyr for data visualization and transformation. Learning either of these languages can significantly enhance an analyst’s ability to work with large datasets and perform advanced analytics.

Tableau and Power BI – Turning Data into Visual Insights

Data visualization is a critical part of analytics, and tools like Tableau and Power BI help analysts create interactive dashboards and reports. Tableau is known for its ease of use and drag-and-drop functionality, while Power BI integrates seamlessly with Microsoft products and allows for automated reporting. These tools enable business leaders to understand trends and patterns through visually appealing charts and graphs.

Google Analytics – Essential for Web Data Analysis

For analysts working in digital marketing and e-commerce, Google Analytics is a crucial tool. It helps track website traffic, user behavior, and conversion rates. Analysts use it to optimize marketing campaigns, measure website performance, and make data-driven decisions to improve user experience.

BigQuery and Hadoop – Handling Big Data

With the increasing volume of data, analysts need tools that can process large datasets efficiently. Google BigQuery and Apache Hadoop are popular choices for handling big data. These tools allow analysts to perform large-scale data analysis and run queries on massive datasets without compromising speed or performance.

Jupyter Notebooks – The Data Analyst’s Playground

Jupyter Notebooks provide an interactive environment for coding, data exploration, and visualization. Data analysts use it to write and execute Python or R scripts, document their findings, and present results in a structured manner. It’s widely used in data science and analytics projects due to its flexibility and ease of use.

Conclusion

Mastering the essential tools of data analytics is key to becoming a successful data analyst. SQL, Excel, Python, Tableau, and other tools play a vital role in every stage of data analysis, from extraction to visualization. As businesses continue to rely on data for decision-making, proficiency in these tools will open doors to exciting career opportunities in the field of analytics.

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aethelflaedladyofmercia · 4 years ago

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Twelve Months - Good Omens fanfic

Happy 31st Anniversary of Good Omens! :D

To celebrate this momentous occasion, I have posted a slightly-sad, slightly-sweet Wake the Snake fic on AO3, because our demon has been napping for a whole Twelve Months, and sometimes Angel gets a little lonely!

Thank you all for another fantastic year in this fandom!

Twelve months.

Aziraphale pushed open the door to Crowley’s flat, a simple shopping bag tucked under his arm.

The lights were still off, the curtains drawn in the awful empty room he called a study. Nothing had changed.

He passed through the enormous, rotating section of wall and into the solarium. This was still bright—many of the plants flourishing despite being unattended so long, despite clearly not having enough water. A few had started flowering. They waved their branches at him as he entered, perking up eagerly.

The angel waved back, but first he peeked into Crowley’s bedroom.

He was still where Aziraphale had left him, on his last visit a month before. Bright red hair spilled across black pillows, grown into a stringy mop. Duvet pulled up to his messily-bearded chin. One hand curled up beside him on the bed.

Still asleep.

With a sigh, Aziraphale crossed over to the plants, who greeted him excitedly, unfurling their newest leaves, a few vines hanging down to brush his face.

“Hello, my lovelies. How are you all doing? Look at you, grown at least a foot since I saw you, I’m sure. And you! What beautiful pink buds. Very impressive.”

He didn’t think Crowley would approve of how he spoke to the plants, but the poor things had been so distraught on his first visit, straining to keep upright, trying to hide their yellowing leaves. So much healthier now, much happier for just a bit of attention. He picked up the watering can and gave them all a quick splash. He didn’t know how much water each needed, but it didn’t seem to matter.

“You keep it up, dears. I’ll be back before you know it.”

Picking up his shopping bag again, Aziraphale headed down the hall to the kitchen. The kettle sat on the island where he’d left it, and he quickly refilled it and set it to boil. While he waited, he pulled his latest creations from the bag: a small pumpkin spice cake from a recipe he’d been perfecting since fall, a lemon coconut cake, and a few apple cinnamon muffins.

Two plates—a muffin for each, a slice of the coconut cake for himself and the pumpkin spice for Crowley.[1] The rest went into the refrigerator, where they would never go bad or stale.

Aziraphale put the plates onto a tray, along with forks and napkins. Next he found two mugs and pulled the little tin of his second-favorite tea out of the bag just as the kettle boiled.

For himself, a teaspoon of the expertly blended leaves, steeped for exactly three minutes, resulting in a pale brown tea with a slightly spicy aroma. For Crowley, he dropped a tea bag into boiling water and let it sit until it was almost black.[2]

He carried the tray back to the solarium and selected a bright red-and-gold tulip that was nearly vibrating in its eagerness to be noticed. A moment to assure the other plants that they were still doing fabulously—particularly a self-conscious little succulent that had rather drooped over the winter but was making a fine recovery—and he once more headed into Crowley’s bedroom.

Crowley had rolled over, and now sprawled on his back, sleeping soundly. He’d apparently kicked a bit, too, as the blanket had slid down past his stomach. Aziraphale smiled as he set the tray on the chair he’d brought in some months ago and got to work.

“It’s wonderful to see you again, dear,” he started cheerfully, carefully rearranging the objects on the little bedside table. “I have a few things for you again, I hope you don’t mind.” Just enough space to slide the mug and the little plate. Perfect.

“I received a package from Tadfield again. Everyone wrote a note and then gathered them all together, really quite clever. They’re all doing well, if a bit bored.” The table was nearly overflowing with little items now, brought in by Aziraphale to cheer the place up. Framed pictures of their human friends, quarantining with their families, clustered in one corner so tightly you could hardly see them anymore.

He pulled the latest out of the shopping bag. “Anathema has started a garden,” he explained, pausing to show the photograph to Crowley’s sleeping form. It showed the witch, kneeling outside her little cottage, working on growing several rows of herbs. “I got the impression she was off to a rough start, but she hopes to send us some mint in the next package. Although Newt warned me not to expect too much, as they’d already forgotten which patch is mint and which is oregano.” He set the picture with the others, and slid the potted tulip alongside it. “I’m sure she could use some advice from you, when you’re ready to share.”

“Nnnnh.” Aziraphale spun eagerly, but no, just Crowley shifting in his sleep again, rolling onto his side.

The angel paused to pull the duvet back up to Crowley’s chin, tugging it straight and smoothing a hand down his back. In a way, his friend was nearly unrecognizable, with that hair and ridiculous beard, but in another way looked the same as ever. That was always Crowley’s way, of course, constantly changing yet somehow always the same.

He lingered, taking in the shape of that face, leaning close, lips hovering above his cheekbone—

Aziraphale pulled back, quickly digging into his bag again. “Oh! Ah, the, um, the children have been making projects for their art class. This past month was sculpture, and they sent us some. Look!” He pulled out four little figures of oven-baked clay. “Ah, young Wensleydale has made a very clever model of a train car. Brian’s is…abstract.” He turned the next a few different ways. “And Pepper’s is, ah, either a very complex symbolic representation of the Patriarchy, or…a troll, I think.” They just fit on the edge of the table, all in a line, a very mismatched tableau. The fourth, on the end, was the best, in Aziraphale’s opinion. “Adam made a little Dog, and it’s very well done, don’t you think?” The canine figure posed with one leg raised and head cocked, ready to play, but the shadow it cast was just a little too large, too ominous, for such a small creature.

With a sigh, Aziraphale shifted the row this way and that. “I sent a letter to Warlock, over in America, but haven’t heard back since Christmas. I believe they’re very busy with something. Politics. You know how it is.” When the Dowlings had left England, they’d planned to return for a visit the following summer. A global pandemic had had other ideas.

“In any case, that just leaves Tracy and Shadwell. I understand he’s decided to hate the concept of literacy this month, so no word on how his war with the squirrels is going. And Tracy has declared she will spend the summer making a fairy garden. I thought her sketches looked very promising, and she promised to send us an update in June. I’m sure you’ll find it charming.”

“Hrrrrm.” Crowley sank under the duvet, nestling down a little deeper. Aziraphale smiled, settling into the chair with his plate and mug.

“Things are loosening up again,” he explained, taking a bite of cake. Delicious, if he said so himself. Sharp and not too sweet. “People are getting vaccinated, shops opening up. It’s really a lovely breath of fresh air, at least when you’re not wearing a mask.” A long sip from his mug, then he held it, fingers tapping. “It’s been nice walking through the park again, just in time for the baby ducks. And that record shop at the corner, they’ve had some wonderful new additions. Which reminds me.”

Putting aside his mug, Aziraphale dug through the bag again and pulled out a handful of square plastic cases. “They had a whole shipment of those little records the Bentley likes. Modern music. I picked out the ones with the rudest names. I’m sure you’ll enjoy them.” He pulled out the first disc and placed it atop Crowley’s phone. The device blinked in confusion a few times, then obediently copied all the music.

“Of course, it’s not all good news.” He stacked the rest of the discs atop the phone and returned to his tea. “Reopening means the customers are coming back. Yesterday, this one individual spent almost an hour browsing the same three shelves. And then he tried to make off with one of my books.” Another long sip. “Granted, he offered to pay, but still. What sort of establishment does he think I’m running?”

Aziraphale paused, waiting for Crowley to respond, not that he ever did. The demon’s eyelids moved a little, but no more.

Sighing, Aziraphale turned to his muffin. “You know, many times in the last year, I’ve wished you were there. Particularly during reopening phases. You could have posed as a customer, and then I’d be able to tell people I was at the capacity limit. Oh, and the people who would call to try and buy my rarest books. Collectors, or so they claimed, but then they just turn around and sell to anyone for twice the price! I’m sure you’d have some biting things to say about such people.” He smiled at Crowley’s sleeping face. “I’ve missed that, and your jokes. Rather more than I expected to.”

When his plate and tea were finished, Aziraphale set them on the floor and reached again into the bag. “Now, I have been attempting to teach my computer how to use the internet. I think it’s going quite well. Adam and his friends gave me a ‘homework assignment’ to find articles on recent news events, and I made the most wonderful discovery. Did you know that humans now share their news through humorous pictures? I printed out my favorites to show you.”[3]

He flicked through a few. “Ah, to start with, a few months ago there was this American politician with amusing mittens who showed up everywhere for a few days. It was extremely droll.” He leaned closer, holding them up for Crowley to see. “Ah, a few more from America. The murder hornets arrived, though by that point everyone had forgotten them. The election became increasingly confusing, and it all ended in a parking lot. For a little while everything was ‘This-or-That Total Landscaping,’ and before that everything was cake.” He showed a few extremely clever illusions. “I did try to make my own, but couldn’t manage it without miracles, which I felt was cheating.”

Really, leaning like this was starting to strain his back. Aziraphale shifted to sit on the edge of the bed, the better to share his pictures. “Ahhh. Also for a time everyone’s calendars were stuck on ‘March.’ And then earlier this year, a group of people learned how the stock market works, but sadly not how to spell it. The whole situation seemed very much like the sort of thing you’d be involved in. And…Oh, this angel from a television show was sent to Hell for…reasons.” He glanced at the shape beside him. Crowley had curled in slightly, pressing against Aziraphale’s back. “Yes. Various reasons. And then this musician, I suppose, went on his own. Both had many people extraordinarily upset.”

The next few images would really tickle Crowley, if he could actually see them. “The biggest news is that a large ship got stuck sideways in that canal in Egypt. Stopped half the world’s shipping for a few days while they dug it out! I’m sure you would have liked that very much. Exactly your sort of trouble. The humans were all very excited.”

The final photo was another of the ship, an image Aziraphale had made himself, printing out a blank version and writing on it in felt-tip pen. The hull of the enormous ship was labeled, “An eternity putting up with the tedious bureaucracy and frequently conflicting commands of my superiors until I begin to doubt my own judgement and sanity,”[4] while the small digger working steadily beside it was “Crowley.”

Aziraphale watched the demon beside him, not really expecting a reaction, certainly not getting one. He reached over, brushing brilliant hair back from Crowley’s forehead. “I think you’d have had rather a lot of fun last year. Or perhaps you’d have been upset you could only watch from a distance. Or…”

He’d leaned much closer than he’d intended, hovering just above Crowley’s forehead.

“Well!” Aziraphale stumbled to his feet. “I suppose that’s just about everything.” He picked up the tray from where he’d rested it on the floor, starting to re-load it with everything he’d brought in. Crowley’s cake and tea sat untouched, as always, but Aziraphale wouldn’t dream of skipping them. “We’re all very optimistic for the summer. Two months and everything should be just…just tickety-boo. Perhaps we can go for that picnic soon, if…yes…”

They’d made such plans for 2020. All the things they would do now they were free. Plans, and other thoughts carried in their minds, possibilities that would play out in their own time. Not too fast, just a slow, steady exploration of everything they could be…

“Well. Pleasant as that idea is, best not to—to plan too much, as the previous year made fools of us all. I just…” He turned away from the tray and watched Crowley sleep, hands clasped before him. “I miss you terribly. And I wish…very much…”

He picked up his shopping bag. One item still inside. The same one he’d been carrying for months, trying to find the courage to bring it out.

With a shaking hand, he reached in and drew forth a soft hand-made doll. He’d spent much of the winter on it. Simple white cotton for the head and body, wooly curls for the hair, and stiff white lace for the wings. Dressed in waistcoat and bowtie made from Aziraphale’s favorite tartan.

He still wasn’t sure why he brought it. He’d stitched several little toys, particularly a lovely black-and-red serpent with gold button eyes that had watched him from the sofa since November. But this, for reasons he couldn’t articulate, this one was for Crowley.

“I, ah…” He shuffled closer, doll clutched in both hands. “I made, um…” Back to the edge of the bed, one hand fumbling across the duvet. “…thought you might like…”

Crowley’s face stood out in stark contrast to the pillow, pale skin and bright hair. Aziraphale wanted to drink it in, memorize every detail, to hold him over until next month. The curve of his nose, the sharp angle of his cheekbones. His lashes flickering as his eyes moved. His lips, pursed ever so slightly…

“Bless it, Angel, are you going to kiss me or not?”

Aziraphale gasped, pulling back from the bright gaze of slit-pupil eyes. “You—you’re awake!”

“Nnnh. Half.” Crowley shifted, head moving across the pillow, eyes threatening to shut again. “Wouldn’t miss your visit.” One hand reached out, plucked the doll from Aziraphale’s unresisting fingers. “For me?”

The angel nodded. “If…if…you like it…or I could—I could just…”

Without a word, Crowley pulled the doll under the duvet and curled up, tucking it under his chin, a faint smile on his lips.

“If you were awake you—you should have said something! I’ve been going—going off like a fool all this—oh!” Aziraphale could feel his face turning hot as he recalled a few times his tongue had been a bit too loose for propriety.

“Mmmmmh.” The golden eyes were shut again.

“Crowley?” No response. “Crowley!” Aziraphale scowled. “Anthony J. Crowley, if you’ve fallen asleep again, I swear, I’ll—”

He’d do what? The angel fumed, but what could he really threaten? To stay away? Never.

“Alright then, I suppose I’ll see you in June. I’ve had several new requests for extremely rare manuscripts and I need to go pen some responses reprimanding these vultures for their cheek. I can—”

“You can stay.”

He spun around. Crowley had one eye barely cracked open. Gently, he pulled back the duvet, showing there was just enough space for Aziraphale beside him.

“I…I couldn’t.” But he stepped forward, not back. “I have business tomorrow, things to—”

“Just tonight then.”

His fingers brushed the mattress and pulled back as if burned. “You—you don’t really mean this, you’re just talking in your sleep.”

“Nah.” Crowley settled the doll by his pillow, making space. “Why else would I give you my key?”

“I…to…water the plants?”

“They take care of themselves.” Crowley held open his arms, eyes shut once more. “I missed you, too.”

Well. What could he say to that?

Aziraphale took off his shoes and slid into bed, into Crowley's arms. They wrapped around him gently as Crowley wriggled closer. “Mmmm. Y’r softer than the doll.”

“Oh.” He’d been called soft many times, generally as a way to imply he was a failure as an angel. But just this once, it made him feel rather pleased. “Soft is good?”

“Verrrry good.” Crowley twisted a bit, trying to find a comfortable way to rest his long limbs, and finally settled curled up against Aziraphale’s chest, tucked below the angel’s chin with a leg hooked over his knees.

The angel smiled. “And you’re…you’re noodlier than a stuffed snake. Err…”

A chuckle, just a stirring of breath across his throat. “Can’t wait to hear the story behind that.” Crowley nuzzled against his shoulder with a sigh. “Good night, Angel.”

Aziraphale swept the brilliant hair back again and bent down, pressing his lips to Crowley’s forehead. A soft, gentle kiss that made his friend smile a little more broadly. “Good night, my dear.”

Crowley drifted off again, burrowing close, as the angel continued to gently tease the back of his hair. Perhaps, he thought, perhaps tomorrow's work wasn't so very urgent. Perhaps a bit of rest would do him good. And perhaps...

Well. Don't plan too much. But for the first time, Aziraphale felt a bit of optimism about the coming summer and its possibilities.

“Sleep well, Crowley.”

[1] Crowley had invented pumpkin spice, and Aziraphale assumed he must like it. In truth, Crowley despised it, and regretted every autumn how it took over the entire world. He missed apple cider season. [2] Aziraphale had suspected since the early 1950s that Crowley secretly took his tea with several lumps of sugar, but would continue to pretend he didn’t know until Crowley confessed. Considering current circumstances, that was unlikely to be any time soon. [3] Aziraphale’s fax machine, revived after over three decades of disuse, had been somewhat confused to be asked to perform any task at all, much less to print memes onto photo paper with perfectly balanced color; but like the plants and Crowley’s phone, it couldn’t stand to disappoint the angel. [4] It was possible he hadn’t quite mastered this new form of communication.

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10kiaoi · 5 years ago

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A special to kick off Villain Day. ;D

And for the 007 Fest Anon prompt: Hannibal 00q AU

Notes: Unbetaed as always. Canon typical violence. It’s a Hannibal AU- mentions of murder and stuff, no blood. Art of a tableau under cut.

His guest is running late, table placement set yet seat left empty. It’s an unforgivable rudeness to be left wondering if he’s been stood up after all, he’s rather miffed about it in all honesty.

There are no messages or missed calls left on his cell.

Accepting the offer of a wine list, he picks one that won’t demolish his palate entirely before the meal even begins. He’s determined to make the best of the outing- it hasn’t been easy getting a reservation at this particular establishment.

He considers the likelihood of his guest meeting an unfortunate fate- the tube breaking down, an accident on the road, perhaps a regrettable mugging. Even a last minute call to duty by Scotland Yard.

Distress is an unusual affliction; he finds himself loathing the depth of it.

The yelling starts out small, an indignant exclamation that quickly morphs into uncontrolled outrage.

He wonders how such a beast has been allowed to step foot into the restaurant at all.

Such rowdiness isn’t his purview and given the establishment, staff will no doubt be along soon enough to see to it. No reason to ruin a perfectly good night with such uncouth displays despite an overwhelming desire to put the table knife to good use.

A quick glance at the reflection in a fortuitously placed wine glass stretches his patience to its ends. It’s endurance of the highest magnitude that prevents him from acting rashly.

The maître d' goes waltzing past his table briskly and he takes a moment to spare the courteous and hardworking man a sympathetic glance.

----

“They found a tableau.”

The young man slides smoothly onto the bench next to him. He pauses, his graphite study momentarily losing all draw. The pencil is all too heavy in his hand though its point is pathetically dulled by use.

As far as he can tell, Q has come completely unarmed.

He obediently passes both sketchbook and graphite pencil over when Q motions for them.

“It was quite the dreadful scene down at the docks. You can’t imagine the chaos when the Mister and Missus of the vessel turned up after getting the call,” Q states glibly.

Amusingly, Q starts correcting his study. A shade darker here, a warped line there. Slowly, the warship starts looking less like a jumble of scribbles and more like a black and white photograph.

The stern comes into sharp focus, then the masts and hull. Finally, Q etches out the bow, paying loving attention to the bowsprit.

His eyes follow the pencil strokes, enthralled by the graceful movements.

“They were utterly infuriated- poor Missus fainted right on the spot. I dare say the vessel won’t be in their hands much longer, not with a Double Oh having graced its decks,” Q continues.

He tilts his head in askance.

Q remains silent for a moment, gaze distant.

He wonders what Q is reliving that very moment when it’s clear his mind is no longer here, in the museum where his physical self sits.

Finally, Q whispers with no small amount of trepidation, “I think it was Seven making a statement.”

----

“A figurehead?” he queries later in the privacy of his office, reaching for a bottle of Merlot.

Q nods. “Why,” Q gripes, “why a trust fund baby with his hands in several of the biggest rental agencies with exorbitant rental rates at that -” He cuts himself off with a frustrated groan, sprawling messily over the sofa.

He pours two glasses, offers one to Q who accepts it gratefully.

“It’s just, he was a toff and a powerful one too, offshore accounts would do that- hardly worth the effort and risk of capture,” Q mumbles, biting his lip, “it’s not pathological to Seven- what has he got to gain?”

“Besides satisfaction from the act of killing?”

The look Q shoots him is particularly venomous. He hides his smile behind his glass.

Back in his own sofa seat, he drinks in the view before him. Of an aggravated young man, wonderfully brilliant and oh so enticing. Marvels at Q’s state of mind- how desperate he is to understand, to see, to empathise.

All it takes is a little nudge and he’s perfectly posed to help Q come unto a higher comprehension.

He sets his glass on a side table, leaning forward on his sofa. It earns him Q’s full focus, hazel eyes eyeing his motion attentively as if he has answers to all of Q’s questions.

It’s more than a little intoxicating to be the one person Q looks to in his time of need.

“The people of the past used to dedicate many hours to carve figureheads out of solid wood,” he offers, “They believed the figureheads were powerful symbols of protection that would guide their vessels through treacherous seas.”

“Seven believed… The benefits outweigh the risks?” Q ventures uncertainly, brows furrowing in deep thought. “The tableau was left there for us to find...”

“A message,” he interprets, blood rushing with anticipation, “think Q, use that big brain of yours. What is the tableau saying?”

“It was meant to be found, to be seen, be celebrated.” Q lunges up from his seat, pacing anxiously between the window and the sofas. “He’s absolutely elated, he’s found something, no, someone- a safe harbour.”

His eyes follow Q’s frantic pacing around the room. Close, so close-

“Chester’s slaughter was just the cherry on top- well deserved for his injustice against society by not paying his dues. It’s justice wrapped up in a nice little package, like a message in a bottle-” Q grinds to a halt in the middle of the room.

“Oh, oh, ” Q flushes, eyes impossibly wide, “It’s a declaration of courtship.”

He eyes the way Q’s tongue pops out to wet his lips, throat working through several false starts. He savours the flash of realisation in those lovely irises, even as he sits primed to react.

“I appreciate the sentiment, if not the delivery,” Q breathes wryly.

----

Q slides into the unoccupied seat at the table with an aggravated sigh, having finally escaped the poorly behaved fellow diner.

The plain little business card he hands over slips into the depths of Bond’s suit jacket, with the other diners none the wiser.

“He thought I was a waiter and took offence at my refusal to serve him.,” Q grumbles when he looks at him in askance, “I could feel your distaste a mile away.”

He smiles, absurdly pleased by his partner’s consideration.

#00q #007 fest #007 fest 2020 #teambondvillains #quartermaster #james bond #007 #art by op #fanfics #what's the tag for villains day again #look nbc hannibal led me to 00q #it has a special place in my non existent heart #:'D #hms 00q

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digitaldataera · 4 years ago

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Learn About Different Tools Used in Data Science

Data Science is a very broad spectrum and all its domains need data handling in unique way which get many analysts and data scientists into confusion. If you want to be pro-active in finding the solution to these issues, then you must be quick in making decision in choosing the right tools for your business as it will have a long-term impact.

This article will help you have a clear idea while choosing the best tool as per your requirements.

Let's start with the tools which helps in reporting and doing all types of analysis of data analytic and getting over to dashboarding. Some of the most common tools used in reporting and business intelligence (BI) are as follows:

- Excel: In this you get wide range of options which includes Pivot table and charts, with which you can do the analysis more quickly and easily.

- Tableau: This is one of the most popular visualization tools which is also capable of handling large amounts of data. This tool provides an easy way to calculate functions and parameters, along-with a very neat way to present it in a story interface.

- PowerBI: Microsoft offers this tool in its Business Intelligence (BI) Space, which helps in integrations of Microsoft technologies.

- QlikView: This is also a very popular tool because it’s easy to learn and is also a very intuitive tool. With this, one can integrate and merge, search, visualize and analyse all the sources of data very easily.

- Microstrategy: This BI tool also supports dashboards, key data analytics tasks like other tools and automated distributions as well.

Apart from all these tools, there is one more which you cannot exclude from this tool's list, and that tool is

- Google Analytics: With google analytics, you can easily track all your digital efforts and what role they are playing. This will help in improvising your strategy.

Now let's get to the part where most of the data scientists deal with. The following predictive analytics and machine learning tools will help you solve forecasting, statistical modelling, neural networks and deep learning.

- R: It is very commonly used language in data science. You can access its libraries and packages as they are easily available. R has also a very strong community which will you if you got with something.

- Python: This is also one of the most common language for data science, or you can also say that this is one the most used language for data science. It is an open-source language which makes it favourite among data scientists. It has gained a good place because of its ease and flexibility.

- Spark: After becoming open source, it has become one of the largest communities in the world of data. It holds its place in data analytics as it offers features of flexibility, computational power, speed, etc.

- Julia: This is a new and emerging language which is very similar to Python along-with some extra features.

- Jupyter Notebooks: This is an open-source web application widely used in Python for coding. It is mainly used in Python, but it also supports R, Julia etc.

Apart from all these widely used tools, there are some other tools of the same category that are recognized as industry leaders.

- SAS

- SPSS

- MATLAB

Now let's discuss about the data science tools for Big Data. But to truly understand the basic principles of big data, we will categorize the tools by 3 V's of big data:

· Volume

· Variety

· Velocity

Firstly, let's list the tools as per the volume of the data.

Following tools are used if data range from 1GB to 10GB approx.:

- Microsoft Excel: Excel is most popular tool for handling data, but which are in small amounts. It has limitations of handling up to 16,380 columns at a time. This is not a good choice when you have big data in hand to deal with.

- Microsoft Access: This is also another tool from Microsoft in which you handle databases up to 2 Gb, but beyond that it will not be able to handle.

- SQL: It has been the primary database solution from last few decades. It is a good option and is most popular data management system but, it still has some drawbacks and become difficult to handle when database continues to grow.

- Hadoop: If your data accounts for more than 10Gb then Hadoop is the tool for you. It is an open-source framework that manages data processing for big data. It will help you build a machine learning project from starting.

- Hive: It has a SQL-like interface built on Hadoop. It helps in query the data which has been stored in various databases.

Secondly, let's discuss about the tools for handling Variety

In Variety, different types of data are considered. In all, data are categorized as Structured and Unstructured data.

Structured data are those with specified field names like the employee details of a company or a school database or the bank account details.

Unstructured data are those type of data which do not follow any trend or pattern. They are not stored in a structured format. For example, the customer feedbacks, image feed, video fee, emails etc.

It becomes really a difficult task while handling these types of data. Two most common databases used in managing these data are SQL and NoSQL.

SQL has been a dominant market leader from a long time. But with the emergence of NoSQL, it has gained a lot of attention and many users have started adopting NoSQL because of its ability to scale and handle dynamic data.

Thirdly, there are tools for handling velocity.

It basically means the velocity at which the data is captured. Data could be both real time and non-real time.

A lot of major businesses are based on real-time data. For example, Stock trading, CCTV surveillance, GPS etc.

Other options include the sensors which are used in cars. Many tech companies have launched the self-driven cars and there are many high-tech prototypes in cue to be launched. Now these sensors need to be in real-time and very quick to dynamically collect and process data. The data could be regarding the lane, it could be regarding the GPS location, it could be regarding the distance from other vehicles, etc. All these data need to be collected and processed at the same time.

So, for these types of data following tools are helping in managing them:

- Apache Kafka: This is an open-source tool by Apache and is quick. One good feature of this tool is that this is fault-tolerant because of which this is used in production in many organisations.

- Apache Storm: This is another tool from Apache which can used with most of the programming language. It is considered very fast and good option for high data velocity as it can process up to 1 Million tuples/second.

- Apache Flink: This tool from Apache is also used to process real-time data. Some of its advantages are fault-tolerance, high performance and memory management.

- Amazon Kinesis: This tool from Amazon is a very powerful option for organizations which provides a lot of options, but it comes with a cost.

We have discussed about almost all the popular tools available in the market. But it’s always advisable to contact some data science consulting services to better understand the requirements and which tool will be best suitable for you.

Look for the best data science consulting company which would best suit in your requirements list.

#data science #data science consulting services #best data science consulting company #data science consultant

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itraininginchandigarh · 4 years ago

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Advanced Excel Training in Mohali

Our Advanced Excel Training in Mohali is completely looking to provide this one of the best Microsoft Excel training. Our Microsoft Excel training centre is equipped with the perfect environment to learn with all the required facilities. We guide our Excel training students to be certified at end of our course. In the last six months’ timeframe, we have completed Microsoft Excel training for more than 78+ students with excellent feedback and placements.

We are charging very competitive in the market which helps to bring more Excel experts into this market. Our Microsoft Excel training course fee is very affordable which anyone can pay on an installment basis as well. We are having various class timings which suit everyone who wants to learn in their own timings. Our Microsoft Excel training will be scheduled in regular weekdays and weekends based on the student’s request. We are giving more Microsoft Excel training for corporates which employees of their company will be benefits with our training.

Advanced Excel course training is created for aspiring to become successful Data Analysts or aspiring to start their careers in the field of Analytics. Advanced Excel Courses aid students in becoming world-class financial analysts in firms such as private equity, investment banking, equity research, corporate development, and Financial Planning and Analysis sectors. Advanced Excel Training in Mohali will show you how to use Excel better, faster and more efficiently.

Here, you will learn how to save time, approach new features and work with confidence. Our teaching procedure makes sure maximum comprehension in the shortest possible time. We provide a good learning environment with lots of practical hands-on exercises and real-world examples.

What is

Excel and Advanced Excel?

Excel is a spreadsheet program used to save, modify and recover numerical data in a grid format of columns and rows. Excel is the basis for almost every profession for entering, calculating and analyzing data such as product sales, figures, plan budgets, chart data, sales taxes or commissions. Microsoft Excel is the standard in the business world; excel software comes in a Microsoft office package of productivity software. There are many other excel software in the market some or specific to the business process, and some are from a general point of view.

It can also be used to draw out data from external sources such as stock market feeds, automatically updating the data through different formulas in real-time. Excel possesses the ability to perform powerful computing capabilities in the field of financial and scientific computation capabilities. Professions like Administrative Assistant, Information Clerk, Accountants, Auditors, Cost Estimator, Financial Analyst, Sales Manager, and much more practice excel more in their working procedure to complete tasks.

Advanced Excel Course Eligibility Factor

This course certification holds no eligibility criteria. However, interested candidates from numerous quantitative backgrounds, looking for Advance Excel training and looking for job oriented scopes, must obtain some knowledge on analytics and reporting skills using MS-Excel, VBA, MS-Access, SQL, and Tableau.

Who can attend for

Advanced Excel?

The Advanced Excel Course in Mohali is framed for professionals who are already familiarized with the fundamentals of Microsoft Excel and who wish to operate with more advanced features of Microsoft Excel. They will become well organized in working with worksheets, evaluating data, forming MIS reports, and automating various tasks.

Anyone wishing to enter into the domain of data analysis and wishes to work in a more competent manner

Students who want to have a good job and hence learn the advanced aspects of Excel. For example, MBA and Engineering students can enhance this course.

Any businessperson who is disposed to maintain his/her business record in an efficient manner and keep track of the business progress in a timely manner.

MIS Reporting Executives

Financial professionals & Auditors

Admin & HR Executives

Top Management & Team Heads

Sales & Marketing Professionals

Advanced Excel Training Course Content

Pivot and Pivot Graphs:

Creating of Pivot Table

Using the Summary function in Pivot Table

Pivot Table Calculations

Creating own formulas in Pivots

Grouping and Ungrouping in Pivot Tables

Creating a Pivot Graph

Custom Filter on Pivot Table

Working on Objects:

Inserting Objects

Putting various files into Excel (like .doc, .pdf, .txt files)

Hyperlinking to different sheets

Providing screen tips in the hyperlink

Hyperlink to send emails

Inserting symbols

Sending a working file to an email

Data Management in Excel - Sorting:

Rearranging of Data

Sorting by alphabets, numbers and time

Filtering Data:

Using the Auto filter option

Custom Filtering with different options

Advanced Filtering

Find Unique records using advanced filtering

Filter using operators AND/OR

Filter by color

Grouping and Outlining Data

Group and ungroup rows and columns

Auto outlining the data

Freezing and Unfreezing

Data Validation:

Use Data Validation in Excel

Using Input Message in Data validation

Using error message in Data Validation

Removing duplicate records:

Remove duplicate records in a spreadsheet

Customize to remove the duplicate values

Split Texts:

Split texts into different columns

Detach a special character from a string and crack the string

Data Consolidation:

Combining different spreadsheet using data consolidation

Using the summary function in data consolidation

Conditional Formatting:

Use of conditional formatting

Custom formatting

Copy Data:

Use of format painter

Use of paste special

Strikethrough a value

Find and Replace

View Multiple Files:

Arrange multiple windows to view in one time

Split huge sheet into sub sheets for the better inspection

Working on Tab:

Hide/ Unhide a tab

Color a tab

Move /Copy the tab to another sheet/workbook

4 Advanced Functions:

V-Lookup

H-Lookup

ISERROR

Index Match

Rows

Columns

Match

Offset

Get Pivot Data

Date and Time Functions

Text Functions

Operators

Mathematical functions

Calculation options

Name Manager:

Define a name to a range

Use of name manager

Using formulas in the name manager

Editing name range

5 Data Connection from External Data:

From access

From SQL

From Web

From ODBC

6 Customize your Excel view:

Customize Excel view

Developer Option

Trust Centre

Add-Ins

Save directory options

Language Setting

Quick access toolbar

7 Security options in excel:

Protecting the worksheet

Protecting the workbooks

Sharing the workbook

Allow users to edit ranges

Job Opportunities after

Advanced Excel Training

in Mohali

There is no rejecting that Excel is an evergreen tool. Even now, various jobs require Advanced Excel skills. The industry needs experts who can assess the data and aid in the decision making for the management. There is a great demand for the MIS operations that handles the evaluation of data.

Enhancing Gaining MS Excel skills will pave the way for MIS roles, data analysis roles, business analysis role, etc. They can do budgeting and financial modeling in a logical manner, Let’s see some of the in-demand profiles in this field”

Data Analyst

MIS Executive

Big Data Analytics Architect

Big Data Solution Architect

Financial Analyst

Market Research Analyst

Big Data Engineer

The demand for talented MIS experts is seeing a raise now. Several MNCs need professionals who can contribute to the dynamic field of analytics. If you have the desire to learn this evergreen tool and become a professional with data management, then contact Advance Excel Training in Mohali for a free demo session now.

Why choose us for Advanced Excel Training in Mohali?

Provides End to End Training

You can get all the information about Advanced Excel in the training which covers all the tricks and functions within Microsoft Excel. We train you on various ways to collect, sort, modify the existing data. Learning Advanced excels helps you to make the difficult task easier.

Get real-time training experience by live projects

We have Focused not just the theoretical concepts but also it involves the practical classes. Our aim is to provide students all the information with which they can easily get a job and work in any IT industry under any circumstances. We also help in Resume & Interviews Preparation Support

Professional Trainers and Experts

We have one of the best and efficient professional trainers in Mohali who can train you with all their experiences and lead you in a proper direction. We offer Experienced Trainers and Lab Facility

Course content and syllabus based on students requirement

The syllabus can be discussed with the trainer before the beginning of the class so that you can arrange your syllabus and course contents according to your requirements.

100% Placement support provided

We provide 100% placement support to each of our students which would help every individual to get a job after the completion of the course. We train students for interviews and provides Placement in combined companies.

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technologyblogofmohit · 1 year ago

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Tableau Quick Start Package from FEXLE

Do not let the confusing spreadsheets and reports bother you more, get the Tableau Quick Start Package from FEXLE!

We transform your complex data into clear, compelling visualizations that tell powerful stories. Engage your audience, identify patterns, and make data-driven decisions with ease. Our experienced consultants will guide you through every step of the process, ensuring you get the most out of Tableau's powerful features.

Connect with FEXLE now & make smarter decisions to grow your business!

#Tableau Quick Starter Package #Tableau Consulting Services #Salesforce Tableau Integration #Salesforce Consulting

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palinanalytics-blog · 4 years ago

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Today's time top demanding course data science using r programming language, data science using popular Python libraries like Numpy, Pandas, Matplotlib, and seaborn. Do you want to become a data scientist? Palin Analytics provides data science certification courses and training provide by top industry expert trainers. We provide online data science, data science using popular R packages like dplyr and ggplot2, data science with python, you can learn online from Palin analytics institute. you access easily on mobile and laptop. We provide 6-month complete access and Our trainer provides the best interview preparation techniques. We will cover topics SQL, data gathering, python programming language, r programming language, machine learning, data visualization with tableau. Upcoming Batch Starting from May 22nd, 2021 10:00 am – 1:00 pm so quick fast enroll now.

#Become Data Scientist #data science using r #data science with python #online data science course #data science training

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siva3155 · 5 years ago

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300+ TOP LOOKER Interview Questions and Answers

Looker Interview Questions for freshers experienced :-

1. What exactly business intelligence according to you is? Business intelligence is nothing but the combination of approaches that an organization uses for data analysis. The useful data can easily be generated from the bulk information that seems totally useless. The biggest benefit of generating the data is that information and decisions can easily be build up. Many organizations have attained a ton of success because of no other strategy than this. Business intelligence makes sure that one can impose a limit on the competition up to a good extent. There are several other issues that can also be eliminated by gathering the very useful information from the sources that seems highly unreliable. 2. What do you mean by the SSIP? Does it have any direct relation with SQL server? SSIP stands for SQL server integration services. When it comes to performing some important tasks related to both ETL and migration of data, the same is widely adopted. Basically, it is very useful to enable the automatic maintenance of the SQL server and that is why it is considered having close relation with the SQL server. Although maintenance is not required on regular basis, this approach is highly beneficial. 3. Name the three categories in the data flow? These are Transformations, Data Sources and Data Destinations. Users can also define other categories in case the need of same is realized. However, it is not possible that all the features work on that particular category. 4. Is it possible for the businesses to utilize the same resources for Business Intelligence or they need experts? Well, it actually depends on the business. Most of the organizations have realized there is actually no need for this. The current workforce can easily be trained and the most desired outcomes can easily be expected. The fact is it doesn’t take a lot of time to train the employees on this domain. Because BI is a simple strategy, organizations can easily keep up the pace in every aspect. 5. Among the File System Deployment and the SQL server deployment, which one is better and why? Does the information exchange in both of them is possible? Generally, the experts prefer SQL Server Deployment. The reason is it provides quick results and without compromising the safety. Yes, the same is possible. 6. Are you familiar with the cache modes available in Looker? How many of them are present in it? There are three modes basically and all are equally powerful. These are Full cache mode, partially cache mode and No cache mode. 7. What exactly do you know about the Full cache mode in Looker? Basically, this is one of the very powerful modes in which SSIS analyze the entire database. This is done prior to the prime activities. The process continues untill the end of the task. Data loading is one of the prime things in generally done in this approach. 8. Does log have relation with the packages? Yes, they are very closely related to the package level. Even when there is a need for the configuration, the same is done only at the package level. 9. What are the noticeable differences you can find upon comparing DTS and SSIS? DTS stands for Data transformation services while the SSIS stands for SQL Server Integration services. SSIS can handle a lot of errors irrespective of their complexity, size and source. In the other side the error handling capacity of DTS is limited There is actually not Business Intelligence functionality in the DTS while SSIS allow fully Business Intelligence Integration SSIS comes with an excellent development wizard. The same is absent in case of DTS When it comes to transformation, DTS cannot compete SSIS SSIS support .Net scripting while the DTS support X scripting 10. What do you mean by the term drilling in data analysis? Well, it is basically an approach that is used for exploring the details of the data that seems useful. It can also be considered to eliminate all the issues such as authenticity and copyright.

Looker Interview Questions 11. What exactly do you know about the execution of SSIS? There are multiple features for logging and they always make sure of log entries. This is generally taken into consideration when the run-time error declares its presence. Although it is not possible to enable this by default, but it can simply be used for writing messages that are totally customized. There is a very large set of log providers that are fully supported by the Integration services without bringing and problem related to compatibility. It is also possible to create the log providers manually. All log entries can be written into the text files very simply and without any third-party help. 12. What is pivoting? Data can easily be switched from row to column and vice versa. The switching categories related to this are considered as pivoting. Pivoting make sure that no information is left on either row or on column when the same is exchanged by the user. 13. Compare No Cache Mode with Partial Cache Mode? Upon adding the new rows, the SSIS start analyzing the database. The rows are only considered or allowed to enter only if they match with the currently existing data and sometime it creates issues when the rows comes instantly one after one. On the other side the No Cache Mode is a situation when the rows are not generally cached. Users can customize this mode and can allow the rows to be cached. However, this is one after one and thus consumes a lot of time. 14. What exactly do you know about the control flow? All the containers as well as the tasks that are executed when the package runs are considered as control flow. Basically the prime purpose of them is to define the flow and control everything to provide best outcomes. There are also certain conditions for running a task. The same is handled by the control flow activities. It is also possible to run several tasks again and again. This always makes sure of time saving and the things can easily be managed in the right manner. 15. What do you mean by the term OLAP? It is basically a strategy that is used for arranging the multidimensional data. Although the prime goal is analyzing of data, the applications can also be manipulated in case the need of same is realized. It stands for On-Line Analytical Processing. 16. In an analytics project, what are the steps which are important at every stage? Exploration of data Defining problems and the solutions for the same Tracking and Implementation of data Data Modeling Data validation Data Preparation 17. What exactly do you understand by the deployment of packages which are related with the SSIS? For this, there is a file tagged as Manifest file. Actually, it needs to be run with the operation and the same always make sure of authenticated or reliable information for the containers and the without the violation of any policy. Users are free to deploy the same into the SQL server or in the File System depend on the needs and allocation. 18. Can you name the components of SQL Server Integration Service which is considered for hoc queries? For hoc queries, the best available component is OLAP engine. 19. What are the control flow elements that are present in the SQL Server Integration Services? These are Functionality related tasks which are responsible for providing proper functionality to the process Containers which are responsible to offer structures in the different packages Constraints that are considered for connecting the containers, executables in a defined sequence All these elements are not always necessary to be deployed in the same tasks. Also, they can be customized upto a good extent. 20. Can you name a few tools that you can deploy for Data Analysis? The most commonly used tools are RapidMiner, Node XL, Wolfran Aplha, KNIME, SOLVER, Tableau, as well as Fusion Tables by Google. 21. Name the methods that are helpful against multi-source problems? Identification of records that are similar ad second is the restructuring of schemas. 22. In data analysis, what you will call the process that places the data in the columns and in the rows? This is generally called as the process of slicing. Slicing always makes sure that the data is at its defined position or location and no errors could be there due to this. 23. According to you, what are the prime qualities that any expert data analyst must have? The very first thing is the right skills with right ability to collect, organize and disseminate big data and without comprising with the accuracy. The second big thing should be robust knowledge of course. Technical knowledge in the database domain is also required at several stages. In addition to this, a good data analyst must have leadership quality and patience too. Patience is required because gathering useful information from a useless or unstructured data is not an easy job. Analyzing the datasets which are very large in size need time to provide best outcomes in few cases. 24. Which container in a package is allowed for logging of information to a package log? Every container or the task is allowed to do this. However, they need to be assigned during the initial stage of the operation for this. 25. Name a few approaches that you will consider for the data cleaning? Any general method can be applied for this. However, the first thing to consider is the size of the data. If it is too large, it should be divided into the small components. Analyzing the summary statistics is another approach that can be deployed. Creating utility functions is also very useful and reliable. 26. What do you understand by the term Logistic regression? It is basically an approach that is considered for proper verification of a dataset that contains independent variables. The verification level is based on how well the final outcome depends on these variables. It is not always easy to change them once defined. 27. How well can you define data flow? It is basically a task that is executed with the help of an SSIS package and is responsible for data- transformation. The source and the destination are always well defined and the users can always keep up the pace with the extensions and modifications. This is because the same is slowed up to a very good extent and users are always free to get the desired information regarding this from the support sections. 28. What are the basic issues in the data that can create a lot of trouble for the data analyst? One of the biggest trouble creators is the duplicate entries. Although this can be eliminated, there is no full accuracy possible. This is because the same information is generally available in a different format or in other sentences. The common misspelling is another major trouble creator. Also, the varying value can create a ton of issues. Moreover, values that are illegal, missing and cannot be identified can enhance the chances of various errors and the same affect the quality up to a great extent. 29. What are the two common methods that can be deployed for data validation? These are Data verification and Data screening. Both these methods are identical but have different applications. 30. What do you mean by the term data cleansing? It is nothing but the other name of data cleaning process. Basically, there are many approaches that are considered for eliminating the inconsistencies and errors from the datasets. Combination of all these approaches is considered as data cleansing. Basically, all the approaches or methods have a similar target and i.e. to boost the quality of data. Looker Questions and Answers Pdf Download Read the full article

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sciforce · 6 years ago

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Best Libraries and Platforms for Data Visualization

Image credit: unslpash.com

In one of our previous posts we discussed data visualization and the techniques used both in regular projects and in Big Data analysis.

However, knowing the plot does not let you go beyond theoretical understanding of what toll to apply for certain data. With the abundance of techniques, the data visualization world can overwhelm the newcomer. Here we have collected some best data visualization libraries and platforms.

Data visualization libraries

Though all of the most popular languages in Data Science have built-in functions to create standard plots, building a custom plot usually requires more efforts. To address the necessity to plot versatile formats and types of data. Some of the most effective libraries for popular Data Science languages include the following:

The R language provides numerous opportunities for data visualization — and around 12,500 packages in the CRAN repository of R packages. This means there are packages for practically any data visualization task regardless the discipline. However, if we choose several that suit most of the task, we’d select the following:

ggplot2

ggplot2 is based on The Grammar of Graphics, a system for understanding graphics as composed of various layers that together create a complete plot. Its powerful model of graphics simplifies building complex multi-layered graphics. Besides, the flexibility it offers allows you, for example, to start building your plot with axes, then add points, then a line, a confidence interval, and so on.

ggplot2 is slower than base R and rather difficult to master, it pays huge dividends for any data scientist working in R.

Lattice

Lattice is a system of plotting inspired by Trellis graphics. It helps visualize multi-variate data, creating tiled panels of plots to compare different values or subgroups of a given variable. Lattice is built using the grid package for its underlying implementation and it inherits many grid’s features. Therefore, the logic of Lattice should feel familiar to many R users making it easier to work with.

RGL

rgl package is used to create interactive 3D plots. Like Lattice, it’s inspired by the grid package, though it’s not compatible with it. RGL features a variety of 3D shapes to choose from, lighting effects, various “materials” for the objects, as well as the ability to make an animation.

Python

The Python Package Index has libraries for practically every data visualization need, however, the most popular ones offering the broadest range of functionalities are the following:

Matplotlib

Matplotlib is the first Python data visualization and the most widely-used library for generating simple and powerful visualizations in the Python community. The library allows building a wide range of graphs from histograms to heat plots to line plots. Matplotlib is the basis for many other libraries that are designed to work in conjunction with analysis. For instance, libraries like pandas and matplotlib are “wrappers” over Matplotlib allowing access to a number of Matplotlib’s methods with less code.

Seaborn

An example of a popular library, built on top of Matplotlib, is Seaborn. Seaborn’s default styles and color palettes are much more sophisticated than Matplotlib. Beyond that, Seaborn is a higher-level library, so it is easier to generate certain kinds of plots, including heat maps, time series, and violin plots.

Bokeh

Similar to the ggplot library for R, Bokeh is based on The Grammar of Graphics. It supports streaming, and real-time data. Unlike the majority of other data visualization libraries, Bokeh can create interactive, web-ready plots, which can easily output as JSON objects, HTML documents, or interactive web applications. Bokeh has three interfaces with varying degrees of control to accommodate different types of users from users wishing to create simple charts quickly to developers and engineers who wish to define every element of the chart.

Scala

Python and R remain the leading languages for rapid data analysis, however, Scala is becoming the key language in the development of functional products that work with big data, as the latter need stability, flexibility, high speed, scalability, etc

Vegas

Probably, the most functional Scala library for data visualization, Vegas allows plotting specifications such as filtering, transformations, and aggregations. It is similar in structure to Python’s Bokeh and Plotly. Vegas provides declarative visualization, so that the user can focus on specifying what needs to be done with the data, without having to worry about the code implementation.

Breeze-viz

Breeze-viz is based on the prominent Java charting library JFreeChart and has a MATLAB-like syntax. Although Breeze-viz has much fewer opportunities than MATLAB, matplotlib in Python, or R, it is still quite helpful in the process of developing and establishing new models.

Javascript

Javascript may not be among languages adopted for Data Science, but it offers vast opportunities for data visualization, and many libraries for other languages are actually wrappers for JS packages.

D3.js

D3 is called the mother of all visualization libraries, since it is the basis for many libraries. Being the oldest library, it remains the most popular and extensive Javascript data visualization library. It uses web standards and is framework agnostic, working smoothly with any Javascript framework. D3 is built for manipulating documents based on data and bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the capabilities of modern browsers without coupling to a proprietary framework, combining visualization components and a data-driven approach to DOM manipulation.

Chart.js

Chart.js is a lightweight library that has fully responsive charts including Bar, Bubble, Doughnut, Line, PolarArea, Radar, Scatter. This is an open-source library based on HTML5. V.2 provides mixed chart-types, new chart axis types, and beautiful animations. Designs are simple and elegant with 8 basic chart types, and you can combine the library with moment.js for time axis.

Online platforms for Data Visualization

At a certain time, especially at the beginning of a project, it is important to generate a lot of charts quickly, in order to explore the scope, depth and the texture of the data and find interesting stories to develop further. There are quite a lot of online platforms to generate data visualization.

Plotly

Plotly as an online platform for data visualization, that is, among all can be accessed from an R or Python Notebook. Plotly is an advanced, online data visualization program with a colorful design. Its forte is making interactive plots, but it offers some charts you won’t find in most packages, like contour plots, candlestick charts, and 3D charts. You can use the chart studio to create web-based reporting templates. You can also modify your own dashboards and interactive graphs for your collaborators to comprehend better.

Tableau

Tableau is a business intelligence system that has a new approach to data analysis. Tableau lets you create charts, graphs, maps and many other graphics. A big advantage of Tableau is availability of several versions: desktop, server, and a cloud one. You can create and publish dashboards, share them with colleagues, and analyze using different methods. We recommend it because of its simplified drag-and-drop system, all day technical support, and flexible package fees.

Of course, it is just a small fraction of all platforms, tools and libraries available for you to visualize your data in the most effective and transparent way. The data itself, as well as the project goals — be it scientific analysis, business intelligence or creating a website that should incorporate some charts — will prompt you the approach, or, most usually a combination of approaches from quick online plotting to base functions and specialized packages.

#data visualization #data science #big data #technology #programming

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datamathstat · 7 years ago

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“Productive stupidity means being ignorant by choice. … The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries” – The importance of stupidity in scientific research

Short read:

An interesting blog post series ‘Probability & Statistics for Data Science’ with simple and important statistic’s definitions.

Artificial Intelligence: 6 Step Solution Decomposition Process There are six key steps to the Solution Decomposition Process to undertake before deploying AI solutions to derive and drive business value.

Identify and Understand Your Targeted Business Initiative

Identify Your Stakeholders and Constituents

Identify Key Decisions

Identify Predictive Analytics

Brainstorm Data That Might Be Better Predictors of Performance

Implement Technology

“oceans of data are useless without a vessel to navigate them. Data-driven companies will be the winners in the digital economy. To that end, it is critical to invest heavily in infusing the discipline of data science into your company for the benefit of your customers and employees. It all starts with a data framework like the one we use within Jabil.”

Data-driven companies will be the winners in the digital economy.

Don’t Follow the Money; Follow the Customer! Superior understanding of customers’ behaviors and preferences and product usage patterns form the basis for industry transformations.

Some information about Big Data Certifications!

22 Differences Between Junior and Senior Data Scientists

Microsoft R Open 3.5.1 now available

The AI Hierarchy of Needs

Did Python declare victory over R? “I don’t think so, because R is an excellent platform with tremendous depth and breadth, which is widely used for data analysis and visualization, and it still has about 50% share. I expect R to be used by many data scientists for a long time, but going forward, I expect more development and energy around Python ecosystem.”

The modern open source data science ecosystem: Python, Anaconda, scikit-learn, Tensorflow, Keras, and Apache Spark. R has small positive associations with Apache Spark, SQL, and Tableau.

Long Read:

Python Data Science handbook By Jake VanderPlas, all the info can be found here. The book was written and tested with Python 3.5. The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python: it’s a fast-paced introduction to the Python language aimed at researchers and scientists.

Mastering Machine Learning with Python in Six Steps: This book is your practical guide towards novice to master in machine learning with Python in six steps. The six steps path has been designed based on the “Six degrees of separation” theory that states that everyone and everything is a maximum of six steps away. Note that the theory deals with the quality of connections, rather than their existence. So a great effort has been taken to design eminent, yet simple six steps covering fundamentals to advanced topics gradually that will help a beginner walk his way from no or least knowledge of machine learning in Python to all the way to becoming a master practitioner. This book is also helpful for current Machine Learning practitioners to learn the advanced topics such as Hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and the basics of reinforcement learning.

Natural Language Processing with Python This book provides a highly accessible introduction to the field of NLP. It can be used for individual study or as the textbook for a course on natural language processing or computational linguistics, or as a supplement to courses in artificial intelligence, text mining, or corpus linguistics. The book is intensely practical, containing hundreds of fully-worked examples and graded exercises. The book is based on the Python programming language together with an open source library called the Natural Language Toolkit (NLTK). NLTK includes extensive software, data, and documentation, all freely downloadable from http://www.nltk.org/.

If you want to read more? here you find a selection of 100 books!!

Amazon newest arrivals

From Bruce Ratner Linkedin post

Data Science Summer Reading List "Productive stupidity means being ignorant by choice. ... The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries…

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geeksperhour · 7 years ago

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via Screaming Frog

A few months ago I posted an article that outlined some of the tools & techniques we use at Screaming Frog for coming up with content ideas. Within that post I touched upon several websites that are worth visiting when you’re in need of inspiration, and I wanted to expand on that aspect of ideation by hearing from several experts in the content marketing field.

If you work in content, it’s a good idea to build up a bank of sites, newsletters and Twitter accounts that you find insightful and check them on a daily basis. Not only does it help inspire you, it highlights topics and forms of content that are currently working well, which you can then use to inform your strategy.

A full list of all the sources mentioned below can be found at the bottom of the post.

Hannah Smith

Head of Outreach – Verve Search | @hannah_bo_banna

I recommend BrainPickings.org to just about everyone I meet. It’s run by Maria Popova, who writes extensively about the creative process. It’s an absolute delight, plus as she’s been writing since 2006 there’s an extensive archive to explore, plus she sends out a weekly newsletter.

Austin Kleon is a writer who draws. He authored Steal Like an Artist, the book which finally helped me figure out my own creative process. He also sends out a weekly newsletter which I’d recommend subscribing to.

Matt Muir is an excellent human who shares a bunch of interesting stuff on twitter, plus he curates the wonderful Imperica newsletter, Web Curios which you should subscribe to – it’ll brighten up your Friday, I promise.

Newsletters aside, there are a few sites I like to browse for inspiration:

This Is Colossal

Fast Company

The Guardian’s Art & Design section

Dangerous Minds

Some of our favourite examples from Hannah’s list are:

Lovely Photo Manipulations Utilizing Stock Photography

The NSA’s newly declassified propaganda posters are wild

Nigerian women’s elaborate hairstyles – in pictures

Red Devils, Black Bats & Angry Cats: The Wacky Art of Vintage Fireworks Packaging

Danny Ashton

Founder and CEO – NeoMam Studios | @dannyashton

/r/dataisbeautiful

FlowingData

FiveThirtyEight

I have also been checking out Data USA to what data might be around the topic we are looking at.

Right now I am really loving using both Ahrefs and Buzzsumo to pull out relevant high performing content to get the creative juices flowing.

The evergreen score on Buzzsumo is great for filtering out time-sensitive news stories.

I also like to do keyword searches on the AskReddit subreddit, it’s great at identifying the top questions for a niche.

Some of our favourite examples from Danny’s list are:

Who kissed whom in The Office (US)

A Day in the Life of Americans

Gun Deaths in America

Mark Johnstone

Creative Content Consultant – MarkJohnstone.co | @epicgraphic

NYTimes Graphics on Twitter plus they do an annual roundup of their best visual storytelling.

Washington Post do the same (Twitter and roundup).

BloomBerg are also doing great stuff these days.

My absolute favourite is VisualisingData’s roundups. I also like to keep an eye on Pudding.cool, and /internetisbeautiful and /dataisbeautiful.

The IIB Awards is probably the best curation at the moment.

Plus I always pay attention to what David McCandless is doing on his own site, Information is Beautiful.

In truth, a lot of things cross my path from following Volodymyr Kupriyanov. VK (as he’s known) was my colleague at Distilled, data analyst/journalist/visualisation expert, and an awesome resource if anyone needs any research or data wizardry on their projects. He’s freelance these days :)

I used to follow ChartPorn & iLoveCharts back in the day, but they seem a little less active these days. Still worth checking out the back catalogue if you haven’t already (might take you a while!).

If anyone wants to learn more about visualising data, they should read Tufte’s book and all of the Fell in Love with Data blog (I’ve read it page to page 3 times!). And follow VisualisingData (again!).

And FlowingData is also a good place for inspiring content.

I also tend to see what other agencies are doing – at awards ceremonies or in conference presentations.

The Webby’s also show lots of very cool content – maybe something to aim for one day! :)

Some of our favourite examples from Mark’s list are:

What Music Do Americans Love the Most? 50 Detailed Fan Maps

What #eclipse2017 looked like across the country

All the Buildings in Manhattan

Seven Things to Know About Climate Change

Stacey MacNaught

Freelance SEO Consultant – StaceyMacNaught.co.uk | @staceycav

I’m a big fan of data driven content, so I like to keep an eye on Gov.uk Statistics for newly released data sets. I also like the Yougov blog as they publish regular data findings there and it’s always interesting to see what stories are then picked up further and to unpick what makes them interesting and newsworthy. You can also learn tonnes from following people who regularly create content you love. The guys at Verve Search spring to mind as immediately obvious ones. Follow them all!

But some of the very best inspiration for me comes from sitting in a room with a couple of other freelancers talking about what we’re seeing in the press and throwing ideas around over a coffee. You can’t beat people to sound off.

Some of our favourite examples from Stacey’s list are:

Car driving test data by test centre

YouGov’s Beer Map of Britain

Ryan Sammy

Creative Strategy – Frac.tl | @ryansammy Finding endless inspiration for high-quality content ideas can be challenging. Most people assume ideation (“idea generation”) is a well that can dry up — especially when you’re stuck brainstorming campaign ideas for a boring vertical. At Fractl, we collectively curate a database of inspiration that our Creative Strategists (“project managers”) use to discover new visualization tactics and generate engaging ideas. If you’re feeling stuck, here’s a quick list of our favorite sites for content inspiration:

Kaggle – a goldmine of free datasets waiting to be turned into campaigns! I use Kaggle to get an idea of how people are taking and manipulating existing datasets. Oftentimes, Kaggle helps inspire an correlating idea, or it gives you a new way to examine a dataset you didn’t even consider.I also pay attention to the forks in projects and the competitions.

Data.world – a social media community that is a cross of Github and Kaggle, where users and organizations share their data and projects. Here, you can search keywords related to your client, or follow a specific topics like technology. I personally set up alerts on new projects and popular shares for the vertical(s) we work with the most frequently.

Tableau Gallery – get inspiration from a community of data analysts and content creators! Discover new ways to visualize data based on what catches your eye, and check out what projects people are already creating in your vertical.

Flowing Data – a site dedicated to curating the cutting edge of data visualization. Pro tip: Bookmark tags from specific publishers you’re hoping to land press from (ex. New York Times) to explore the types of data stories and visualizations that these writers are interested in covering.

Curated Content Emails – There are dozens of email lists you can subscribe to for content inspiration. Two of my favorites are Data Is Plural (archives) and Numlock news (archives), both of which curate popular stories that focus on data analysis. These email blasts are a great way to see how a wide variety of publishers are covering data stories.

Github – Saved Github searches like “visualizations” or “scrapers” are an easy way to get inspired by scrapes other data analysts are doing, and they can expose you to new data scraping techniques others are using. The most important aspect of successful content creation is your ability to distill complex data into a format that will quickly resonate with a broad range of readers. These tools will get you started on your mission to create shareable content for your business!

Some of our favourite examples from Ryan’s list are:

Street network orientation in major cities

All the results from FIFA World Cups

Bangalore’s Bus Routes

Shannon McGuirk

Head of PR & Content – Aira | @shannonmcguirk_

Here’s a few sites that the team and I regularly visit:

TeleGraphs (this is great for trending data)

Creative Bloq

Futurism

Infographicsly

Daily Infographic

Entrepreneur Infographics

BBC Infographics

PR Examples (this is great for seeing how PR agencies are leveraging online assets)

Matador Network Infographics

The Guardian Datablog

A few Reddit threads:

/r/datasets

/r/mapporn

/r/infographics

And Twitter profiles:

Telegraph_Data

Ashley_J_Kirk

Ollie

DataDhrumil

Some of our favourite examples from Shannon’s list are:

How Pep Guardiola’s Manchester City broke the Premier League record

Habitable Planets – Could we make them?

Most popular girl name by state from 1960-2012

Three rebrands stores for the thing in Russia

Rebecca Brown

Head of Content – Builtvisible | @rebeccagraceb

Our team find inspiration in a number of places, and like to take in information in a number of ways.

One of our senior consultants told me she listens to the Stuff You Should Know podcast from howstuffworks.com. That podcast is really interesting because it just covers so much, and you get loads of niche information you wouldn’t find anywhere else!

Our design team obviously look for more visual sources of inspiration, and regularly look at look at Muzli, Dribbble and Behance for design. When it comes to web design, one of our visual designers recommends Awwwards.

We also all share news stories and interesting campaigns constantly, which helps give us loads of relevant inspiration for reactive ideas. In every niche we work on we share weekly news updates, but aside from that we have a huge culture of sharing which really helps inspire everyone. We use a tool called raindrop to collate visual inspiration for all teams.

Some of our favourite examples from Rebecca’s list are:

Narwhals: Unicorns of the Sea

99 design inspiration accounts to follow on Instagram

The History of Connection

Emojis: A New Language?

Sources of Inspiration Mentioned

Subreddits

DataIsBeautiful

AskReddit

InternetIsBeautiful

Datasets

MapPorn

Infographics

Data Driven

Flowing Data

FiveThirtyEight

Telegraph Data

The Guardian Data

Visualising Data

Pudding.cool

Information Is Beautiful

Fell in Love with Data

Tableau Gallery

Datasets

Data USA

Data.world

YouGov

News

The Guardian Art & Design

Bloomberg Graphics

Entrepreneur Infographics

BBC.com Infographics

Awards & Galleries

Information is Beautiful Awards

Webby Awards

Awwwards

Visual.ly

Infographicsly

Dribbble

Behance

Twitter Accounts

Matt_Muir – Chief Writer at Imperica

NYTGraphics – The New York Times Graphics Department

V_kupriyanov – Freelance researcher and data analyst

Telegraph_Data – The home of TeleGraphs – a hub of data journalism from The Telegraph

Ashley_J_Kirk – Senior data journalist at The Telegraph

Ollie – Senior writer fivethirtyeight

DataDhrumil – Database Journalist

Blogs

Creative Bloq

The Inspiration Grid

Shutterstock Blog

Canva Blog

Best Infographics

Bored Panda

Futurism

Daily Infographics

PR Examples

Matador Network

Muz.li

Dangerous Minds

Fast Company

This Is Colossal

Chart Porn

I Love Charts

Podcasts

StuffYouShouldKnow

Newsletters

Brainpickings

Austin Kleon

Data is Plural

Numlock

Web Curios

Tools

Ahrefs

Buzzsumo

Books

The Visual Display of Quantitative Information

Misc

Github Search – visualization

Github Search – scrapers

#IFTTT #Screaming Frog

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crookedtreepoetry · 4 years ago

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Prime Data Science Programs Online

The project is the ultimate step within the Data Scientist Master's Program and will allow you to to show your expertise in knowledge science to employers. This Tableau certification course helps you grasp Tableau Desktop, a world-extensive utilized data visualization, reporting, and business intelligence tool. Advance your profession in analytics by learning Tableau and how to best use this coaching in your work.

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Explore more on - data science course in hyderabad with placements

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Hours: Sunday - Saturday 7 AM - 11 PM

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fullstackanalytics · 7 years ago

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In-Demand Skills You Need to Land Top Big Data Jobs in 2018

It is the right time for IT professionals to acquire big data skills, since big brands such as Amazon, Google, IBM, and other multinational corporations are offering handsome salary packages to data science professionals who can help them in exploiting large and widely disparate sets of data. As big data matures beyond buzzwords, business organizations across the world have started realizing the potential of big data and are now setting up teams to derive business value from the huge caches of data they produce every day. If you are in the market for a big data job, here are the most in-demand skills that you must have to land a job in 2018.

1. Apache Hadoop

Hadoop has been in demand for the last few years and today it has become synonymous with big data and data science. Though it is a decade old technology, the demand for Hadoop professionals has not reduced. In 2017, we witnessed a surge in the demand for professionals skilled in Hadoop technology and this year too we are expecting more of the same. Professionals with sound knowledge of the core components of Hadoop such as Hive, MapReduce, Flume, HDFS, Oozie, HBase, Pig, and YARN will be highly in demand.

2. Apache Spark

With the rising need for quick analysis and processing of large and widely disparate sets of data, Spark is being preferred over MapReduce as a faster and simpler alternative. In the last few years, Spark has become the fastest growing big data skill, which is increasing by 120% year-over-year. Owing to the dearth of skilled Spark professionals in the market, big data experts having sound knowledge of Spark are greatly rewarded by employers.

3. Data Science and Analytics

Raw and unstructured data is worthless until it has been churned by data analysts. As business organizations across the world are looking forward to harnessing the power of big data, they are actively searching for big data experts trained in the interdisciplinary science of data analysis. This includes programming, data modeling, mathematical and statistical know-how and general business acumen.

4. Data Visualization

The demand for data visualization skills has increased dramatically by 2574% in the last 5 years, and this has been closely observed by Burning Glass Technologies, a startup based out of Boston. With the rise in the demand for data visualization skills, enterprises are looking for professionals who are well-versed with different data visualization tools like Tableau and Qlikview.

If you also want to fuel your big data career, it is the right time to hone the most in-demand data skills by going through big data certification and training programs online.

#big data #data science #machine learning #hadoop

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databytesindia-blog · 8 years ago

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Android Training in Bangalore

If we analyze the number of devices connected to the Internet, more than 60% of them are Smartphone’s. And 90% of those Smartphone’s are powered by Android. Hence, the demand of android applications and responsive websites have increased exponentially. In this article, you’ll learn in detail about Android Training in Bangalore. But first, let’s have a quick look at what makes Android so popular?

So why exactly is Android so popular?

Even though there are so many Operating Systems available in the market, such as iOS, Windows etc, Android literally dominates every other competitor in this platform. One reason for that is that unlike the former Operating Systems, Android is freely available for any company to use in their handsets. Whereas iOS is only used by Apple and only Microsoft uses Windows. Hence, in its early days, every smartphone manufacturer opted for Android, as this decreased the production cost of creating an own OS for themselves.

Secondly, it has a really easy User Interface and almost everyone learned it without any hassle. In spite of being easy, it had a geek side too, which impressed the nerds out there. There are hundreds of complex stuff which is only doable using Android. Moreover, in some cases, Android is used for penetration testing as well! Therefore, it basically managed to keep the both techie and non-techie sides happy.

Apparently, all these good things were wrapped with the unbeatable marketing strategy of Google. Thus, the success.

Why learn Android development?

Like I said earlier, Android rules the mobile Operating System market. And the recent smartphone boom in India created a huge urge for app development. Plus, Android apps are popular because unlike websites, a user does not have to remember any address or does not have to open any browser to access the content. And also, because, most of the apps work offline – the user does not need an Internet connection to access the content on the go.

And android has high demand in the IT sector too! So, if you want to make a career in Android development you should obviously opt for a training course in Android. And factually, the salary package of an android developer is way higher than normal PHP or web developers.

Android Training in Bangalore

Many institutions provide Android development training in Bangalore. But not all of those courses are industry standard. A perfect android dev course should include the following features:

Basics of Java language

Android architecture

Android Studio

Database communication

So before starting your android training, don’t forget to check whether your institute teaches you these stuff during the training. Because if they don’t you’re choosing the wrong place.

Even if you’re not interested in getting a job as an android developer, for e.g. say, you want to get placed as a Java backend developer. Still, you should do this certification course because this will add a feather to your resume and thus keep you step ahead of other competitors.

Wrapping up.

Android OS will only grow over time. And it will surely extend to other platforms such as desktop in near future. So, you will never regret getting trained in Android. It’s worth every penny.

DataBytes; leading IT Training Institutes in Bangalore offers Software Testing Courses (Selenium, QTP [Quick Test Professional]), AWS [Amazon Web Services], RPA [Robotic Process Automation], PHP, Tableau / Qlikeview, Aws Devops, Java, UI UX Web Development, Spark, Scala, Adv Java, Python, Android, SQL /PL SQL, Dot NET, ETL, Hadoop, BIG Data, Informatica, AutoCAD etc.

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