#data analytics and data science

[edeptoffpage]

Data Analytics Courses in India: Top Online & Offline Programs

Introduction

In the data-driven world we live in today, the need for professionals with expertise in data analytics is on the rise. If you are a graduate or a working professional wishing to upskill, selecting the best course is imperative. EDEPT is a top provider of data analytics education in India that provides well-rounded programs that will help students gain the necessary skills for today's data-driven world.​

Why Select EDEPT for Data Science and Analytics Courses

EDEPT's data analytics courses are specifically designed to fit the industry demands so that by the time they graduate, the students are already job-ready. Their courses are created in coordination with industry partners to include the newest tools and technologies in data analytics.​

Key Highlights of EDEPT's Best Data Analytics Courses:

Industry-Relevant Curriculum: The courses are formulated in coordination with industry partners in order to encompass the newest tools and technologies available in data analytics.

Hands-On Learning: Practical training via live projects and case studies.​

Expert Instructors: Mentorship from expert professionals with real-world experience in data science and analytics.​

Flexible Learning Options: Facility to learn through both online and offline modes as per the students' preferences.​

Career Support: Personal placement support and career guidance to make students job-ready for placements at top companies.​

Top Colleges and Universities in India

Through taking courses in EDEPT's best data analytics courses, students develop proficiency in must-have tools like Python, R, SQL, and Tableau, and they establish a strong statistical analysis and machine learning skill set.​

The Rising Need for Data Analytics Professionals in India

The need for experienced data analytics professionals in India is increasing, with sectors from various industries looking for professionals who can analyze intricate data to inform strategic decisions. EDEPT's courses work to bridge the educational knowledge gap and industry needs, such that their graduates are extremely valuable in the market.​

EDEPT vs. Other Data Analytics Courses in India

Though there are many data analytics courses in India, EDEPT stands out with its broad syllabus, qualified teachers, and good career guidance. Online courses are offered by institutes such as Simplilearn and Coursera, but EDEPT offers a blended model of study where both online and offline classes are available, appealing to more types of learners.

Conclusion

EDEPT provides some of the finest data analytics courses in India, blending theoretical learning with practical implementation to equip students for successful careers in data science and analytics. Whether you are a fresh graduate or an existing professional seeking to upskill, EDEPT offers the resources and support necessary to thrive in this fast-paced industry.

Ready to advance your data analytics career? Stop by EDEPT's Data Analytics courses to discover more and sign up today.

[hackeocafe]

How To Learn Math for Machine Learning FAST (Even With Zero Math Background)

I dropped out of high school and managed to became an Applied Scientist at Amazon by self-learning math (and other ML skills). In this video I'll show you exactly how I did it, sharing the resources and study techniques that worked for me, along with practical advice on what math you actually need (and don't need) to break into machine learning and data science.

[stemgirlchic]

data analysis is rly like

it's so over we're so back it's so over we're so back it's so over

i know, i know, negative results don't make my work any less valid but cmon please give me something

[studisstudying]

4/7 exams ✅

Honestly the exams were better than expected for the amount I studied, more specifically the way I studied, it seems like I can't get a hold on how I should study and just end up stuffing my brain with information which I will forget soon...? And also I'm too tired to study because of the travelling 🥲I really need to learn how to drive. Anyways here are the cats I saw and also cat motivation to study over the weekend ^^

~✿

[woolieshubris]

thinking about how in one of the more recent videos Kurtis was like. I know my audience it's mostly lesbians. and that episode of make some noise where sam reich was like. yeah our audience is 20 smth women. and how jermas audience analytics is like. 40% women 20% nonbinary . and how then northernlion has a 2% female viewership ranking.

[datascienceunicorn]

The Data Scientist Handbook 2024

HT @dataelixir

[wyyrmwood]

This is WAY different from my usual art, but I'm posting it anyway! I start GIS masters soon and before then I'm practicing making maps in Esri tutorial projects. I'm trying to do one a day, or at least like every other day. This Arctic Sea Ice Decline map took me around 4-5 hours to make, and I changed it from the instructions because I wanted clearly defined decade chunks rather than a gradient map of ice presence over time lol (also just wanted to see if I remembered how to do it). The tutorial provides data sets to use and it's my job to assemble and analyze the raw data and visualize it using ArcPro into something recognizable.

but yea expect to see more little maps n such I suppose

[37-feral-raccoons]

comp sci majors who also hate generative AI reblog please I need to know some people in my field are sane 😭

[datasciencewithmohsin]

Understanding Outliers in Machine Learning and Data Science

In machine learning and data science, an outlier is like a misfit in a dataset. It's a data point that stands out significantly from the rest of the data. Sometimes, these outliers are errors, while other times, they reveal something truly interesting about the data. Either way, handling outliers is a crucial step in the data preprocessing stage. If left unchecked, they can skew your analysis and even mess up your machine learning models.

In this article, we will dive into:

1. What outliers are and why they matter.

2. How to detect and remove outliers using the Interquartile Range (IQR) method.

3. Using the Z-score method for outlier detection and removal.

4. How the Percentile Method and Winsorization techniques can help handle outliers.

This guide will explain each method in simple terms with Python code examples so that even beginners can follow along.

1. What Are Outliers?

An outlier is a data point that lies far outside the range of most other values in your dataset. For example, in a list of incomes, most people might earn between $30,000 and $70,000, but someone earning $5,000,000 would be an outlier.

Why Are Outliers Important?

Outliers can be problematic or insightful:

Problematic Outliers: Errors in data entry, sensor faults, or sampling issues.

Insightful Outliers: They might indicate fraud, unusual trends, or new patterns.

Types of Outliers

1. Univariate Outliers: These are extreme values in a single variable.

Example: A temperature of 300°F in a dataset about room temperatures.

2. Multivariate Outliers: These involve unusual combinations of values in multiple variables.

Example: A person with an unusually high income but a very low age.

3. Contextual Outliers: These depend on the context.

Example: A high temperature in winter might be an outlier, but not in summer.

2. Outlier Detection and Removal Using the IQR Method

The Interquartile Range (IQR) method is one of the simplest ways to detect outliers. It works by identifying the middle 50% of your data and marking anything that falls far outside this range as an outlier.

Steps:

1. Calculate the 25th percentile (Q1) and 75th percentile (Q3) of your data.

2. Compute the IQR:

{IQR} = Q3 - Q1

Q1 - 1.5 \times \text{IQR}

Q3 + 1.5 \times \text{IQR} ] 4. Anything below the lower bound or above the upper bound is an outlier.

Python Example:

import pandas as pd

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate Q1, Q3, and IQR

Q1 = df['Values'].quantile(0.25)

Q3 = df['Values'].quantile(0.75)

IQR = Q3 - Q1

# Define the bounds

lower_bound = Q1 - 1.5 * IQR

upper_bound = Q3 + 1.5 * IQR

# Identify and remove outliers

outliers = df[(df['Values'] < lower_bound) | (df['Values'] > upper_bound)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Values'] >= lower_bound) & (df['Values'] <= upper_bound)]

print("Filtered Data:\n", filtered_data)

Key Points:

The IQR method is great for univariate datasets.

It works well when the data isn’t skewed or heavily distributed.

3. Outlier Detection and Removal Using the Z-Score Method

The Z-score method measures how far a data point is from the mean, in terms of standard deviations. If a Z-score is greater than a certain threshold (commonly 3 or -3), it is considered an outlier.

Formula:

Z = \frac{(X - \mu)}{\sigma}

 is the data point,

 is the mean of the dataset,

 is the standard deviation.

Python Example:

import numpy as np

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate mean and standard deviation

mean = df['Values'].mean()

std_dev = df['Values'].std()

# Compute Z-scores

df['Z-Score'] = (df['Values'] - mean) / std_dev

# Identify and remove outliers

threshold = 3

outliers = df[(df['Z-Score'] > threshold) | (df['Z-Score'] < -threshold)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Z-Score'] <= threshold) & (df['Z-Score'] >= -threshold)]

print("Filtered Data:\n", filtered_data)

Key Points:

The Z-score method assumes the data follows a normal distribution.

It may not work well with skewed datasets.

4. Outlier Detection Using the Percentile Method and Winsorization

Percentile Method:

In the percentile method, we define a lower percentile (e.g., 1st percentile) and an upper percentile (e.g., 99th percentile). Any value outside this range is treated as an outlier.

Winsorization:

Winsorization is a technique where outliers are not removed but replaced with the nearest acceptable value.

Python Example:

from scipy.stats.mstats import winsorize

import numpy as np

Sample data

data = [12, 14, 18, 22, 25, 28, 32, 95, 100]

Calculate percentiles

lower_percentile = np.percentile(data, 1)

upper_percentile = np.percentile(data, 99)

Identify outliers

outliers = [x for x in data if x < lower_percentile or x > upper_percentile]

print("Outliers:", outliers)

# Apply Winsorization

winsorized_data = winsorize(data, limits=[0.01, 0.01])

print("Winsorized Data:", list(winsorized_data))

Key Points:

Percentile and Winsorization methods are useful for skewed data.

Winsorization is preferred when data integrity must be preserved.

Final Thoughts

Outliers can be tricky, but understanding how to detect and handle them is a key skill in machine learning and data science. Whether you use the IQR method, Z-score, or Wins

orization, always tailor your approach to the specific dataset you’re working with.

By mastering these techniques, you’ll be able to clean your data effectively and improve the accuracy of your models.

[uditprajapati7685]

Pickl.AI offers a comprehensive approach to data science education through real-world case studies and practical projects. By working on industry-specific challenges, learners gain exposure to how data analysis, machine learning, and artificial intelligence are applied to solve business problems. The hands-on learning approach helps build technical expertise while developing critical thinking and problem-solving abilities. Pickl.AI’s programs are designed to prepare individuals for successful careers in the evolving data-driven job market, providing both theoretical knowledge and valuable project experience.

[hackeocafe]

Statistics - A Full Lecture to learn Data Science (2025 Version)

Welcome to our comprehensive and free statistics tutorial (Full Lecture)! In this video, we'll explore essential tools and techniques that power data science and data analytics, helping us interpret data effectively. You'll gain a solid foundation in key statistical concepts and learn how to apply powerful statistical tests widely used in modern research and industry. From descriptive statistics to regression analysis and beyond, we'll guide you through each method's role in data-driven decision-making. Whether you're diving into machine learning, business intelligence, or academic research, this tutorial will equip you with the skills to analyze and interpret data with confidence. Let's get started!

[cybromtech]

Elevate Your Brand, Rule the Digital Space with Cybrom Technology.

[akhandpratapsingh]

Why is Data Science related to Machine Learning?

Data Science and Machine learning — As the name suggests, both of them are inter-related, Ask me how? Well Data Science and Machine Learning are imperatively two main assets of the new-technology related world. In this realm, these two are the same halves of a whole learning. The machine learning acts as an important as well as essential vital ingredient in the data science models. However, both of them are having different responsibilities as well as jobs. Some of the major factors that are underlying that will help you to understand the realm of data science related to machine learning better, so let’s dive into their inter-connection -

1. Machine learning is a pivotal key point in Data Science — As the name suggests, the Data Science helps to extract data and insights from the toolbox. The Machine Learning in Data Science not only helps as a central process to provide algorithms to aid and identify patterns in data, however it can also help in making intelligent decisions or predictions without needing the explicit of any guidance and support.

2. Data Science uses Machine Learning to build up predictive Models — The imperative factor that helps and focuses on data science is making all related models that can easily help and anticipate trends or results. Apart from this, Machine Learning also allows all the data analysts and scientists to create and develop models that help to improve their reachability and performance as they analyze more insights and data. Hence, if you also want to learn more about ML or Data Science, and are looking forward to an end-to-end solution of the learning well has the better solution. To find more, please check out other courses, waiting for you!!

[algorithmquartz]

First Post!

Or at least, this is my first post on Tumblr since high school lol. I used to use Tumblr often a long time ago, but I left. Now I start fresh with a new account!

My name is AlgorithmQuartz. I'm 22, an INFJ, and a Capricorn Sun/Leo Moon/Libra Rising. I'm into gaming, singing, coding, witchcraft, tarot/oracle, astrology, and exploring esoteric ideas. I'm currently in school for computer science with a minor in data analytics. I'm also a practicing witch.

If any of that sounds interesting, feel free to give me a follow! I may also follow back. I've recently started making YouTube content as well, and I'm shortly going to post the couple videos I have so I can put them on the featured section of my blog. If you wanna check out my YouTube channel, then here it is!

Y e e t u s

[samvavlabs]

Data Analyst Roadmap for 2024!

Cracking the Data Analyst Roadmap for 2024! Kick off your journey by mastering and delving into Python for data manipulation magic, and dazzle stakeholders with insights using PowerBi or Tableau. Don't forget, that SQL proficiency and hands-on projects refine your skillset, but never overlook the importance of effective communication and problem-solving. Are you checking off these milestones on your path to success? 📌 For more details, visit our website: https://www.samvavlabs.com  . . . #DataAnalyst2024 #CareerGrowth #roadmap #DataAnalyst #samvavlabs #roadmap2024 #dataanalystroadmap #datavisualization

[datascienceunicorn]

HT @dataelixir