#Python for Data Analytics

[lohithkumar9011]

DATA ANALYTICS COURSE IN GWALIOR

Upgrade your career with DataMites Data Analytics Course in Gurgaon. Learn Python, SQL, Power BI, Tableau, and Machine Learning with expert-led training. Get hands-on experience, industry certification, and internship & job support. Limited-time 65% fee discount!

[techyseeducation]

Data Analytics Training In Marathahalli

Techyse Education in Marathahalli, Bangalore, offers specialized Data Analytics Training in Marathahalli for individuals looking to build expertise in Python, Power BI, and data analysis techniques. Their industry-aligned courses focus on practical learning through real-world projects, ensuring students gain hands-on experience in data manipulation, visualization, and dashboard creation. Whether you are a beginner or an experienced professional, Techyse’s programs are designed to enhance your skill set, making you job-ready for roles in data analytics.

Comprehensive Data Analytics Training in Marathahalli Techyse Education takes pride in delivering high-quality Data Analytics Training in Marathahalli, backed by experienced instructors with deep industry knowledge. The curriculum covers essential tools and techniques, from data wrangling with Python to creating interactive dashboards using Power BI, ensuring students are prepared to meet industry demands. With personalized mentorship, career support, and placement assistance, Techyse provides a well-rounded learning experience. Whether aiming for career growth or a fresh start in data analytics, Techyse Education equips learners with the skills to excel in a competitive job market.

Techyse Education | Data Analyst, Python, Power BI Training in Marathahalli, Bangalore

18, Krishna Summit, 307, 3rd Floor, Aswath Nagar, Next to Canara Bank, Marathahalli, Bangalore, Karnataka 560037

Phone: 098445 14333 Website : https://techyse.in/

Our Google Map Location is : https://maps.app.goo.gl/dLsBM669nKHTutxu9

Follow us: Facebook : https://www.facebook.com/techyse.education/ Twitter: https://x.com/techyse_edu/ Instagram : https://www.instagram.com/techyeseducation/ LinkedIn : https://www.linkedin.com/company/techyse-education/ Youtube: https://www.youtube.com/@TechyseEducation

[hackeocafe]

Python for Data Analytics - Full Course Tutorial

🧮 Course Problems & Certificate 👉 https://lukebarousse.com/python 📝 Course Code & Notes 👉 https://lukeb.co/python_repo

[thebestdataanalyticscourses]

What are the best data analytics courses?

In today's data- driven folks, the capability to dissect and decide perceptivity from vast quantities of information is a largely sought- after skill. Whether you are a freshman looking to dive into the instigative field of data analytics or an educated professional aiming to upskill, chancing the stylish data analytics courses is essential to boost your career.

Introductions that have been involved in the data analysis process include data mining, operation, statistics, and donation of data. The very first step is extracting the data from various sources like huge databases, textbooks, detector data, etc. Using the concept of ETL (extract, transform, load), raw data is converted into a manageable format. This process is called data mining and is the most crucial step in the channel. Data warehousing or operation is the next step in the analysis that implements the databases by deriving.

 To embark on this informational trip, I have curated a collection of top- notch data analytics courses that offer unique perspectives, interactive literacy gests, and comprehensive knowledge.

Data analytics:

Data analytics can help small businesses in several ways. By understanding data analytics, businesses can make better opinions about where to allocate their coffers and how to price their products or services. also, data analytics can help businesses identify trends and understand their client base. Data analytics software can track and dissect data, allowing you to produce practicable reports and dashboards. However, read our companion to the stylish data analytics tools and software available moment if you ’re looking for a dependable result.

 Let's explore these courses together and discover which bones reverberate with your literacy style and pretensions. Data analytics is the part of a data critic to find patterns in the information present to Ameliorate performance and effectiveness. They also need to work through a complete analysis of the channel of data. Which means there can be multitudinous ways to dissect data. These are the top online courses for data analysis. 1. Data Analytics with R and Excel

As a data analyst, Excel is a powerful tool that can help you organize, analyze, and visualize data. Here are some key skills and techniques you can use in Excel for data analysis: Excel provides various functions and tools to clean and transform your data. You can use functions like TRIM, CLEAN, and SUBSTITUTE to remove extra spaces, and special characters, or replace text. Tools like data filters and conditional formatting can help you identify and handle missing or inconsistent data.

2. Big Data Analysis with Python

 Community Support Python has a large and active community that supports and contributes to the development of colourful data wisdom libraries and tools. This community has produced numerous useful libraries, including Pandas, NumPy, matplotlib, and SciPy, which are extensively used in data science.

3. Python for data analytics

 Thanks to Python's focus on simplicity and readability, it boasts a gradational and low literacy wind. This ease of literacy makes Python an ideal tool for neophyte programmers. Python offers programmers the advantage of using smaller lines of law to perform tasks than is demanded when using aged languages. In other words, you spend further time playing and lower time working law.

4. Business Analytics specialization with Tableau

 A powerful BI tool, Tableau is the second most popular one from our survey. It helps extract and gather data stored in multiple locations and comes with an intuitive drag-and-drop interface. Tableau makes data across departments readily available for data engineers and managers to create useful dashboards.

Conclusion:

Data analyst boot camps are suitable for individuals who want to kick-start their careers in data analysis or professionals seeking to upskill and transition into a data-related role. By providing a condensed and focused learning experience, boot camps aim to prepare participants for entry-level data analyst positions or provide them with a strong foundation to pursue further studies or certifications in the field of data analytics. Excel provides built-in data analysis tools, such as regression analysis, descriptive statistics, and goal-seeking. These tools can help you perform advanced analysis, such as identifying trends, finding correlations, forecasting, and performing what-if scenarios.

Here's how you can get started!

[herpersonafire]

Hey everyone! enjoying my (two) week break of uni, so I've been lazy and playing games. Today, working on Python, I'm just doing repetition of learning the basics; Variables, Data types, Logic statements, etc. Hope everyone has a good week!

[valyrfia]

you say the only thing tethering me to this sport is a ship and i vehemently agree with you while trying to shove the python pipeline i built for fun to compare past race telemetry under the table

[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.

[emie-data]

Hi, I’m Emie!

I’m learning Python, building a digital garden, and doing my best to grow gently through it all. My ultimate goal is to start a career in data analytics.

I love cozy aesthetics, soft creativity, and turning quiet moments into meaningful ones. This is my little corner of the internet where I can be myself—bear ears, tea, coding, and all.

I love AI and use it regularly. The image above is AI-generated.

I’m hoping to meet others who are also on their coding journey and maybe join—or build—a little community where we can support each other and grow together toward our goals. 🌿

[pandeypankaj]

What's the difference between Machine Learning and AI?

Machine Learning and Artificial Intelligence (AI) are often used interchangeably, but they represent distinct concepts within the broader field of data science. Machine Learning refers to algorithms that enable systems to learn from data and make predictions or decisions based on that learning. It's a subset of AI, focusing on statistical techniques and models that allow computers to perform specific tasks without explicit programming.

On the other hand, AI encompasses a broader scope, aiming to simulate human intelligence in machines. It includes Machine Learning as well as other disciplines like natural language processing, computer vision, and robotics, all working towards creating intelligent systems capable of reasoning, problem-solving, and understanding context.

Understanding this distinction is crucial for anyone interested in leveraging data-driven technologies effectively. Whether you're exploring career opportunities, enhancing business strategies, or simply curious about the future of technology, diving deeper into these concepts can provide invaluable insights.

In conclusion, while Machine Learning focuses on algorithms that learn from data to make decisions, Artificial Intelligence encompasses a broader range of technologies aiming to replicate human intelligence. Understanding these distinctions is key to navigating the evolving landscape of data science and technology. For those eager to deepen their knowledge and stay ahead in this dynamic field, exploring further resources and insights on can provide valuable perspectives and opportunities for growth 

[lohithkumar9011]

DATA ANALYTICS COURSE IN RANCHI

Elevate your career with DataMites Data Analytics Course in Chandigarh. Learn Python, SQL, Power BI, Tableau, and Machine Learning from industry experts. Get hands-on training, certification, and internship & job support. Limited-time 65% fee discount!

[techyseeducation]

Techyse Education in Marathahalli, Bangalore, offers specialized Data Analytics Training in Marathahalli for individuals looking to build expertise in Python, Power BI, and data analysis techniques. Their industry-aligned courses focus on practical learning through real-world projects, ensuring students gain hands-on experience in data manipulation, visualization, and dashboard creation. Whether you are a beginner or an experienced professional, Techyse’s programs are designed to enhance your skill set, making you job-ready for roles in data analytics.

Comprehensive Data Analytics Training in Marathahalli Techyse Education takes pride in delivering high-quality Data Analytics Training in Marathahalli, backed by experienced instructors with deep industry knowledge. The curriculum covers essential tools and techniques, from data wrangling with Python to creating interactive dashboards using Power BI, ensuring students are prepared to meet industry demands. With personalized mentorship, career support, and placement assistance, Techyse provides a well-rounded learning experience. Whether aiming for career growth or a fresh start in data analytics, Techyse Education equips learners with the skills to excel in a competitive job market.

Techyse Education | Data Analyst, Python, Power BI Training in Marathahalli, Bangalore

18, Krishna Summit, 307, 3rd Floor, Aswath Nagar, Next to Canara Bank, Marathahalli, Bangalore, Karnataka 560037

Phone: 098445 14333 Website : https://techyse.in/

Our Google Map Location is : https://maps.app.goo.gl/dLsBM669nKHTutxu9

Follow us: Facebook : https://www.facebook.com/techyse.education/ Twitter: https://x.com/techyse_edu/ Instagram : https://www.instagram.com/techyeseducation/ LinkedIn : https://www.linkedin.com/company/techyse-education/ Youtube: https://www.youtube.com/@TechyseEducation

[womaneng]

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A post shared by Zeynep Küçük Woman Engineer (@woman.engineer)

ou can become a data analyst ⤵️📈📊💯 Here’s what you need to do: - believe in yourself - learn Excel -learn SQL - learn Tableau - build Portfolio - update Linkedin - optimize Resume - Use Network -apply for jobs That’s the way. . . .

[kookiesdayum]

I want to learn AWS from scratch, but I'm not familiar with it and unsure where to start. Can anyone recommend good resources for beginners? Looking for structured courses, tutorials, or hands-on labs that can help me build a strong foundation.

If you know any resources then plz let me know.

Thanks 🍬

[herpersonafire]

Hey! and welcome back!

As Uni is about to begin in a few days....I upgraded to a third monitor! Also!! I job a Data entry job! Big things moving forward :)

Let me introduce myself;

I'm Mick, (she/her) and I currently study Data Analytics. I'll be 26 in July and I love to read and play video games in my free time. I also like to watch football (LIVERPOOL xx)

Im currently taking the time to learn Excel and Python. However, I know for school I'll be learning Python, SQL and maybe some other things.

FOLLOW me along on this journey and if you have any questions please ask!