Data Analysis Bootcamp™ 21 Real World Case Studies from Udemy

Data Analysts aim to discover how data can be used to answer questions and solve problems through the use of technology. Many believe this will be the job of the future and be the single most important skill a job application can have in 2020.

In the last two decades, the pervasiveness of the internet and interconnected devices has exponentially increased the data we produce. The amount of data available to us is Overwhelming and Unprecedented. Obtaining, transforming and gaining valuable insights from this data is fast becoming the most valuable and in-demand skill in the 21st century.

In this course, you'll learn how to use Data, Analytics, Statistics, Probability, and basic Data Science to give an edge in your career and everyday life. Being able to see through the noise within data, and explain it to others will make you invaluable in any career.

We will examine over 2 dozen real-world data sets and show how to obtain meaningful insights. We will take you on one of the most up-to-date and comprehensive learning paths using modern-day tools like Python, Google Colab and Google Data Studio.

You'll learn how to create awesome Dashboards, tell stories with Data and Visualizations, make Predictions, Analyze experiments and more.

Our learning path to becoming a fully-fledged Data Analyst includes:

The Importance of Data Analytics
Python Crash Course
Data Manipulations and Wrangling with Pandas
Probability and Statistics
Hypothesis Testing
Data Visualization
Geospatial Data Visualization
Story Telling with Data
Google Data Studio Dashboard Design - Complete Course
Machine Learning - Supervised Learning
Machine Learning - Unsupervised Learning (Clustering)
Practical Analytical Case Studies

Google Data Studio Dashboard & Visualization Project:

Executive Sales Dashboard (Google Data Studio)

Python, Pandas & Data Analytics and Data Science Case Studies:

Health Care Analytics & Diabetes Prediction
Africa Economic, Banking & Systematic Crisis Data
Election Poll Analytics
Indian Election 2009 vs 2014
Supply-Chain for Shipping Data Analytics
Brent Oil Prices Analytics
Olympics Analysis - The Greatest Olympians
Home Advantage Analysis in Basketball and Soccer
IPL Cricket Data Analytics
Predicting the Soccer World Cup
Pizza Resturant Analytics
Bar and Pub Analytics
Retail Product Sales Analytics
Customer Clustering
Marketing Analytics - What Drives Ad Performance
Text Analytics - Airline Tweets (Word Clusters)
Customer Lifetime Values
Time Series Forecasting - Demand/Sales Forecast
Airbnb Sydney Exploratory Data Analysis
A/B Testing

What's inside

Learning objectives

Understand the value of data for businesses
The importance of data analytics
The role of a data analyst
Learn to use python, pandas, matplotlib & seaborn, scikit-learn
Learn visualization tools such as matplotlib, seaborn, plotly and mapbox
Hypothesis testing and a/b testing - understand t-tests and p values
Unsupervised machine learning with k-means clustering

Machine learning from linear regressions (polynomial & multivariate), k-nns, logistic regressions, svms, decision trees & random forests
Advanced pandas techniques from vectorizing to parallel processsng
Statistical theory, probability theory, distributions, exploratory data analysis
Ananlytic case studies involving retail, health, elections, sports, resturants, airbnb, uber and more!
Full tutorial on google data studio for dashboard creation
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Understand the value of data for businesses
The importance of data analytics
The role of a data analyst
Learn to use python, pandas, matplotlib & seaborn, scikit-learn
Learn visualization tools such as matplotlib, seaborn, plotly and mapbox
Hypothesis testing and a/b testing - understand t-tests and p values
Unsupervised machine learning with k-means clustering
Machine learning from linear regressions (polynomial & multivariate), k-nns, logistic regressions, svms, decision trees & random forests
Advanced pandas techniques from vectorizing to parallel processsng
Statistical theory, probability theory, distributions, exploratory data analysis
Ananlytic case studies involving retail, health, elections, sports, resturants, airbnb, uber and more!
Full tutorial on google data studio for dashboard creation
Show more
Show less

Syllabus

Course Introduction & the Importance of Data Analysts

Course Introduction

The Importance of Data Analyst

Why Data is the new Oil

You can find all the course Code in the Resources section of this chapter.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores industry-standard Python, Pandas, Matplotlib & Seaborn, Scikit-learn, and Google Data Studio

Suitable for complete beginners in Data Analytics by providing a Python crash course

Teaches advanced Pandas techniques from vectorizing to parallel processing

Taught by Rajeev D. Ratan and Nidia Sahjara, experts in Data Analytics and Data Science

Provides a solid foundation in Probability Theory, Statistical Theory, Distributions, and Exploratory Data Analysis

Develops practical skills through Analytical Case Studies involving Retail, Health, Elections, Sports, Resturants, Airbnb, Uber, and more!

Reviews summary

Practical data analysis with real-world cases

According to learners, this course is a brilliant and practical bootcamp for aspiring data analysts, consistently praised for its focus on real-world case studies that bring complex concepts to life and provide actionable skills. Many commend the instructor's clear and engaging explanations, making topics easy to grasp. While the course offers a comprehensive overview, some students note a balance between breadth and depth, suggesting it provides a strong foundation but may require additional self-study for mastery in advanced areas like machine learning. The Google Data Studio module is frequently highlighted as highly useful for dashboard creation.

Older feedback mentioned outdated content, but recent reviews are clear.

"Some parts of the course felt a bit disorganized and occasionally outdated information crept in, especially concerning specific library versions."

"I felt I needed to seek external resources for deeper understanding on several topics."

"More recent reviews from the latter half of 2023 do not highlight issues with outdated content, suggesting potential improvements or isolated instances."

Pacing may vary for learners with different backgrounds.

"If you're a complete beginner, you might find some parts challenging."

"I came with some Python knowledge, so the crash course was a bit slow, but I appreciate it's there for others."

"I sometimes found the transitions between topics a bit abrupt."

Practical section for dashboard creation.

"The Google Data Studio part was a huge plus for me, very practical for dashboard creation."

"I appreciated the Google Data Studio section as it's very useful for current job market."

Instructor provides clear, engaging, and insightful explanations.

"The instructor explains everything clearly and concisely. Highly recommended for anyone looking to get into data analysis."

"The instructor is knowledgeable and engaging. This course gives you actionable skills."

"The instructor breaks down complex ideas into understandable chunks. The sheer number of case studies is amazing."

"The instructor's explanations are top-notch. It's truly a bootcamp for practical data skills."

Case studies offer invaluable practical application.

"The real-world case studies are excellent, making complex concepts easy to grasp. Highly recommended for anyone looking to get into data analysis."

"Absolutely loved the hands-on approach and the diverse case studies. They truly differentiate this course from others."

"The sheer number of case studies is amazing and makes the learning very engaging. I now feel much more confident in my data analysis abilities."

"The case studies are truly the best part of this course. They demonstrate practical applications well."

Covers many topics, providing breadth, but not deep dives.

"It rushes through some advanced topics without enough foundational depth. It's a decent overview, but not a deep dive."

"I expected more depth on machine learning. It's good for getting a high-level overview, but you won't become an expert from this alone."

"I sometimes wished for more in-depth theoretical explanations for certain machine learning models."

"It's more of a broad introductory course than a deep dive bootcamp."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Analysis Bootcamp™ 21 Real World Case Studies with these activities:

Introduction to Python - Setup and Environment

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This activity will refresh your knowledge of Python coding and ensure you have the proper environment set up for the remainder of the activities.

Browse courses on Python

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Review and revise Python's basic syntax and data types
Set up your local Python environment and ensure it is working properly
Install any necessary libraries and packages required for the course

Read 'Python Data Science Handbook'

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Gain a comprehensive understanding of Python's data science capabilities by reading this highly recommended book.

View Python Data Science Handbook: Essential Tools... on Amazon

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Read and understand the fundamental concepts of data science and Python programming
Follow along with the book's examples and exercises to practice data manipulation, visualization, and machine learning techniques
Refer back to the book as a valuable resource throughout your data science journey

Connect with Experienced Data Analysts

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Seek guidance and insights from experienced professionals in the field of data analytics.

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Attend industry events, meetups, or online forums to connect with potential mentors
Introduce yourself, express your interest in data analytics, and inquire about mentorship opportunities
Establish a mentoring relationship and schedule regular meetings to discuss your progress and receive advice

Six other activities

Expand to see all activities and additional details

Show all nine activities

Pandas Data Manipulation Exercises

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Complete a series of exercises to practice data manipulation and exploration techniques using Pandas!

Browse courses on Pandas

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Import a dataset and familiarize yourself with its structure
Clean and prepare the data by handling missing values and correcting data types
Perform data exploration by calculating summary statistics, grouping, and sorting
Manipulate data by creating new columns, filtering rows, and merging dataframes

Probability and Statistics Exercises

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Solve a variety of exercises to deepen your understanding of probability and statistics concepts.

Browse courses on Probability

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Calculate probabilities using basic rules, conditional probability, and Bayes' theorem
Estimate parameters and conduct hypothesis tests using t-tests and chi-square tests
Analyze data distributions and relationships using correlation and linear regression

Organize a Study Group with Classmates

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Collaborate with classmates to enhance understanding and retention of course material.

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Form a study group with a diverse range of skills and perspectives
Set regular meeting times and establish clear goals for each session
Review course materials together, discuss concepts, and solve problems collaboratively

Create a Dashboard for a Given Dataset

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Design and develop an interactive dashboard to effectively communicate insights from a given dataset.

Browse courses on Dashboard Creation

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Gather and explore the dataset to understand its content and structure
Design the dashboard layout and determine the appropriate visualizations to use
Develop the dashboard using a data visualization tool, such as Tableau or Google Data Studio
Test and refine the dashboard to ensure it is functional, user-friendly, and visually appealing

Attend a Workshop on Data Analytics Techniques

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Enhance your knowledge and skills in specific data analytics techniques through hands-on workshops.

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Research and identify relevant workshops offered by industry professionals or academic institutions
Attend the workshop and actively participate in discussions and exercises
Apply the learned techniques to your data analytics projects and assignments

Participate in Data Analytics Competitions

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Challenge yourself and showcase your skills by participating in data analytics competitions.

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Identify and register for competitions that align with your interests and skill level
Analyze the competition dataset, develop a strategy, and implement your data analytics solutions
Submit your results and receive feedback to further enhance your skills

Career center

Learners who complete Data Analysis Bootcamp™ 21 Real World Case Studies will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst analyzes data to extract insights and help businesses make informed decisions. This course provides a comprehensive overview of the skills and techniques needed to succeed in this role, including data manipulation and wrangling with Pandas, probability and statistics, hypothesis testing, data visualization, and machine learning. With its focus on real-world case studies, this course will help you develop the practical skills and knowledge to become a successful Data Analyst.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use their knowledge of data analysis, statistics, and machine learning to solve complex problems. This course provides a strong foundation in these areas, making it an excellent choice for those who want to pursue a career as a Data Scientist. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians collect, analyze, interpret, and present data to help businesses and organizations make informed decisions. This course provides a solid foundation in statistical theory, probability theory, and distributions, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models. This course provides a strong foundation in machine learning, including supervised and unsupervised learning, as well as advanced topics such as natural language processing and computer vision. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses improve their operations and make better decisions. This course provides a solid foundation in data analysis, statistics, and visualization, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers build and maintain the systems that store and process data. This course provides a solid foundation in data engineering, including data warehousing, data mining, and big data technologies. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Data Engineer

Data Visualization Specialist

Data Visualization Specialists create visual representations of data to help businesses communicate insights and make better decisions. This course provides a solid foundation in data visualization techniques, including matplotlib, seaborn, and plotly. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Data Visualization Specialist

Product Manager

Product Managers are responsible for the development and launch of new products. This course provides a solid foundation in data analysis, statistics, and visualization, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Product Manager

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a solid foundation in probability theory, statistics, and machine learning, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Quantitative Analyst

Risk Analyst

Risk Analysts assess and manage the risks faced by businesses and organizations. This course provides a solid foundation in probability theory, statistics, and machine learning, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Risk Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency of business operations. This course provides a solid foundation in probability theory, statistics, and machine learning, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use data to analyze financial performance and make investment recommendations. This course provides a solid foundation in statistics, probability, and data visualization, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing Analysts use data to measure the effectiveness of marketing campaigns and make recommendations for improvement. This course provides a solid foundation in statistics, probability, and data visualization, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Marketing Analyst

Market Researcher

Market Researchers use data to understand consumer behavior and identify market opportunities. This course provides a solid foundation in statistics, probability, and data visualization, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for Market Researcher

UX Researcher

UX Researchers use data to understand user behavior and improve the user experience. This course provides a solid foundation in statistics, probability, and data visualization, which are essential for success in this role. The course's focus on real-world case studies will also give you the practical experience needed to succeed in this field.

See salaries and explore the career path for UX Researcher