The Data Science Bootcamp 2022: 5 Data Science Projects course is a training program that helps students to know everything about Data Science. It begins with the most basic level of data science and progresses to the most advanced techniques step-by-step.
The subject of data science is expanding so quickly and transforming so many different businesses. The advantages of data science in industry, research, and daily life are enormous. The course covers many subjects, including Python’s foundations, data structures in Python, data cleaning, functions, and the use of Python in Data Science. The course is available for INR 799 on Udemy.
Who all can opt for this course?
- Anyone looking to start a career in data science
- Anyone looking to advance their knowledge of data science
- Anyone interested in a career as a data analyst
- Anyone interested in pursuing a career as a data scientist, including technologists
- Anyone looking to improve their Python abilities
- This course is for those who have never programmed or written scripts in Python
- College graduates looking for work or professionals who desire to enhance their training capacities
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 799 |
| Duration | 15.5 hours |
| Rating | 4.3/5 |
| Student Enrollment | 38,907 students |
| Instructor | Data is Good Academy https://www.linkedin.com/in/dataisgoodacademy |
| Topics Covered | Python fundamentals, Python data structures, data cleaning, query analysis, data visualization, hypothesis testing |
| Course Level | Beginner |
| Total Student Reviews | 691 |
Learning Outcomes
- Fundamentals of Data Science
- Data Science capstone projects
- Grouping and Filtering for Data Analysis
- Python object-oriented programming
- Hypothesis testing
- Basic and advanced Data Visualization
- Clustering analysis
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Python Fundamentals (01 hour 45 minutes) | Why should you learn Python? |
| Installing Python and Jupyter Notebook | ||
| Understanding the Interface of Jupyter Notebook | ||
| Naming Convention for variables | ||
| Built-in Data Types and Type Casting | ||
| Scope of Variables | ||
| Q and A | ||
| Quiz on Variables and Data Types | ||
| Quiz Solution | ||
| Thanks for your support!! | ||
| Arithmetic and Assignment Operators | ||
| Comparison, Logical, and Bitwise Operators | ||
| Identity and Membership Operators | ||
| Quiz on Operators | ||
| Quiz Solution | ||
| String Formatting | ||
| String Methods | ||
| User Input | ||
| Quiz on Strings | ||
| Quiz Solution | ||
| If, elif, and else | ||
| For and While | ||
| Break and Continue | ||
| Quiz on Loops and Conditionals | ||
| Quiz Solution | ||
| 2. | Mastering Python Data Structures (01 hour 37 minutes) | Differences between Lists and Tuples |
| Operations on Lists | ||
| Operations on Tuples | ||
| Quiz on Lists and Tuples | ||
| Quiz Solution | ||
| Introduction to Dictionaries | ||
| Operations on Dictionaries | ||
| Nested Dictionaries | ||
| Introduction to Sets | ||
| Set Operations | ||
| Quiz on Sets and Dictionaries | ||
| Quiz Solution | ||
| Introduction to Stacks and Queues | ||
| Implementing Stacks and Queues using Lists | ||
| Implementing Stacks and Queues using Deque | ||
| Quiz on Stacks and Queues | ||
| Quiz Solution | ||
| Time Complexity | ||
| Linear Search | ||
| Binary Search | ||
| Bubble Sort | ||
| Insertion and Selection Sort | ||
| Merge Sort | ||
| Quiz on Searching, Sorting, and Time Complexity | ||
| Quiz Solution | ||
| 3. | Python Functions Deep Dive (01 hour 15 minutes) | Introduction to Functions |
| Default Parameters in Functions | ||
| Positional Arguments | ||
| Keyword Arguments | ||
| Python Modules | ||
| Quiz on Introduction to Functions | ||
| Quiz Solution | ||
| Lambda Functions | ||
| Filter, Map, and Zip Functions | ||
| List, set, and Dictionary Comprehensions | ||
| Quiz on Anonymous Functions | ||
| Quiz Solution | ||
| Introduction to Aggregate Functions | ||
| Introduction to Analytical Functions | ||
| Quiz on in-Built Functions | ||
| Quiz Solution | ||
| Solving the Factorial Problem using Recursion | ||
| Solving the Fibonacci Problem using Recursion | ||
| Quiz on Recursions | ||
| Quiz Solution | ||
| Introduction to Classes and Objects | ||
| Inheritance | ||
| Encapsulation | ||
| Polymorphism | ||
| Quiz on Classes and Objects | ||
| Quiz Solution | ||
| 4. | Python for Data Science (01 hour 05 minutes) | Introduction to DateTime |
| The date and time class | ||
| The DateTime class | ||
| The time delta class | ||
| Quiz on Dates and Times | ||
| Quiz Solution | ||
| Meta Characters for Regular Expressions | ||
| Built-in Functions for Regular Expressions | ||
| Special Characters for Regular Expressions | ||
| Sets for Regular Expressions | ||
| Quiz on Regular Expressions | ||
| Quiz Solution | ||
| Array Creation using Numpy | ||
| Mathematical Operations using Numpy | ||
| Built-in Functions in Numpy | ||
| Quiz on Introduction to Numpy | ||
| Quiz Solution | ||
| Reading Datasets using Pandas | ||
| Plotting Data in Pandas | ||
| Indexing, Selecting and Filtering Data using Pandas | ||
| Merging and Concatenating DataFrames | ||
| Lambda, Map, and Apply Functions | ||
| Quiz on Introduction to Pandas | ||
| Quiz Solution | ||
| 5. | Data Cleaning (01 hour 27 minutes) | Causes and Impact of Missing Values |
| Types of Missing Values | ||
| When should we delete the missing values | ||
| Imputing missing values with the business logic | ||
| Imputing missing values with Mean/Median/Mode | ||
| Imputing missing values in a real-time scenario | ||
| Quiz on Missing Values Imputation | ||
| Quiz Solution | ||
| How outliers can be harmful to machine learning models | ||
| Finding out outliers from the data | ||
| Using Winsorization to deal with outliers | ||
| Deleting and Capping the outliers | ||
| Dealing with outliers in a real-world scenario | ||
| Quiz on Outliers Treatment | ||
| Quiz Solution | ||
| Introduction to reindex, set_index, reset_index, and sort_index Functions | ||
| Introduction to Replace and Drop level Function | ||
| Introduction to Split and Strip Function | ||
| Introduction to Stack, and Unstack Functions | ||
| Introduction to Melt, Explode and Squeeze Functions | ||
| Data Cleaning on Big Mart Dataset | ||
| Data Cleaning on Movie Dataset | ||
| Data Cleaning on Melbourne Housing Dataset | ||
| Data Cleaning on Naukri Dataset | ||
| 6. | Query Analysis (40 minutes) | Aggregate functions used for Grouping |
| Using Groupby for Grouping Operations | ||
| Groupby with Idxmax and Idxmin functions | ||
| Using Color scales for better visualization | ||
| Visualizing the Groupby Operations | ||
| Using Pivot Tables for Grouping Operations | ||
| Difference between Groupby and Pivot tables | ||
| Performing Cross Tabulation | ||
| Visualizing Cross tabulated Data | ||
| Interactive Grouping Operations | ||
| Quiz on Grouping Operations | ||
| Quiz Solution | ||
| When to perform Filtering Operations | ||
| Introduction to Simple Filtering Operations | ||
| Advanced Filtering Operations | ||
| Filtering and Grouping Operations | ||
| Interactive Filtering Operations | ||
| Quiz on Filtering Operations | ||
| Quiz Solution | ||
| 7. | Data Visualizations (01 hour 31 minutes) | Univariate Analysis |
| Bivariate Analysis | ||
| Multivariate Analysis | ||
| Quiz on Basics of Visualization | ||
| Quiz Solution | ||
| Scatter Plots | ||
| Charts with Colorscale | ||
| Bar, Line, and Area Charts | ||
| Facet Grids | ||
| Statistical Charts | ||
| Polar Charts | ||
| Subplots | ||
| 3D Charts | ||
| Waffle Charts | ||
| Maps | ||
| Quiz on Advanced Visualizations | ||
| Quiz Solution | ||
| Animation with Bubbleplot | ||
| Animation with Facets | ||
| Animation with Scatter Maps | ||
| Animation with Choropleth Maps | ||
| Quiz on Animated Visualizations | ||
| Quiz Solution | ||
| Introduction to Ipywidgets | ||
| Interactive Univariate Analysis | ||
| Interactive Bivariate Analysis | ||
| Interactive Multivariate Analysis | ||
| Quiz on Interactive Visualizations | ||
| Quiz Solution | ||
| Sunburst Charts | ||
| Parallel Co-ordinate Charts | ||
| Funnel Charts | ||
| Gantt Charts | ||
| Ternary Charts | ||
| Tree Maps | ||
| Network Charts | ||
| Quiz on Miscellaneous Charts | ||
| Quiz Solution | ||
| 8. | Statistics and Probability (01 hour 31 minutes) | Why you should learn Statistics and Probability |
| Walking through the course Content | ||
| Applications of Probability in Real Life | ||
| Basic Probability | ||
| Conditional Probability | ||
| Set Theory | ||
| Bayes’ Theorem | ||
| Permutations and Combinations | ||
| Quiz on Probability | ||
| Quiz Solution | ||
| Types of Data | ||
| Measures of Central Tendency | ||
| Measures of Spread | ||
| Measures of Dependence | ||
| Quiz on Descriptive Statistics | ||
| Quiz Solution | ||
| Continuous vs Discrete Distributions | ||
| Introduction to Normal Distribution | ||
| Concept of Skewness | ||
| Using QQ Plots to check Normal Distribution | ||
| Quiz on Statistical Distributions | ||
| Quiz Solution | ||
| Sample Mean and Population Mean | ||
| Central Limit Theorem | ||
| Bias and Variance | ||
| Maximum Likelihood Estimation | ||
| Confidence Intervals | ||
| Quiz on Inferential Statistics | ||
| Quiz Solution | ||
| 9. | Hypothesis Testing (55 minutes) | What is Hypothesis Testing |
| Null Hypothesis and Alternate Hypothesis | ||
| Types of Error | ||
| P-Value and Level of Significance | ||
| Quiz on Hypothesis Testing | ||
| Quiz Solution | ||
| One Sampled T Test | ||
| Two Sampled T Test | ||
| Paired Sampled T Test | ||
| Quiz on Student’s T Test | ||
| Quiz Solution | ||
| One Sampled Z Test | ||
| Two Sampled Z Test | ||
| Quiz on Z Test | ||
| Quiz Solution | ||
| One Sampled ANOVA Test | ||
| Two Sampled ANOVA Test | ||
| Quiz on ANOVA Test | ||
| Quiz Solution | ||
| The goodness of Fit Test | ||
| Test of Independence | ||
| Quiz on Chi-Squared Test | ||
| Quiz Solution | ||
| 10. | Data Exploration (43 minutes) | Why EDA and how it is useful |
| Course curriculum walkthrough | ||
| Data Profiling | ||
| Analyzing Target Data | ||
| Quiz on Introduction to EDA | ||
| Quiz Solution | ||
| Summarizing data | ||
| Exploring the Dabl Library | ||
| Exploring the Sweetviz Library | ||
| Using Color Gradients for better analysis | ||
| Best Practices for Data Exploration | ||
| Quiz on Examining Data | ||
| Quiz Solution | ||
| 11. | Capstone Project 1: Players Performance Analysis (32 minutes) | Understanding the problem statement |
| Setting up the Environment | ||
| Data Cleaning | ||
| Feature Engineering | ||
| Data Visualization | ||
| Query Analysis | ||
| Major Learnings from the project | ||
| Quiz on Players’ Performance Analysis | ||
| 12. | Capstone Project 2: Startups Case Study and Analysis (17 minutes) | Understanding the Problem Statement |
| Setting up the Environment | ||
| Data Cleaning | ||
| Querying the data using Visualizations Part – 1 | ||
| Querying the data using Visualizations Part – 2 | ||
| The major learning from the Project | ||
| Quiz on Startups case study and Analysis | ||
| 13. | Capstone Project 3: Movie Recommender Systems (50 minutes) | Setting up the Environment |
| Taking a Deep Dive into the Dataset | ||
| Understanding the Problem Statement | ||
| Missing Values Imputation | ||
| Top 10 Profitable Movies | ||
| Manipulating the Duration and Language Column | ||
| Extracting the Movie Genres | ||
| Top 10 Most Popular Movies on Social Media | ||
| Analysing Which Genre is Most Bankable? | ||
| Loss and Profit Analysis on English and Foreign Movies | ||
| Gross Comparison of Long and Short Movies | ||
| Association between IMDB Rating and Duration | ||
| Comparing Critically acclaimed Actors | ||
| Top Movies based on Gross, and IMDB | ||
| Recommending Movies based on Languages and Actors | ||
| Recommending Similar Genres and Movies | ||
| Key Takeaways from this Project | ||
| Quiz on Movie Recommender Systems | ||
| 14. | Capstone Project 4: Global Cost of Living (47 minutes) | Setting up Environment |
| Understanding the Dataset | ||
| Understanding the Problem Statement | ||
| Extracting Latitude and Longitude from the Location | ||
| Performing Feature Engineering | ||
| Comparing Lifestyle in different Countries | ||
| Top N and Bottom N Analysis | ||
| Performing Geospatial Analysis | ||
| Comparing different Lifestyle Factors | ||
| Comparing Some of the Most Popular Countries | ||
| Comparing Lifestyle in Indian Cities | ||
| Ranking Places based on their cost of living | ||
| Analysing Cost of Essential Items | ||
| Analysing Quality of Life | ||
| Suggesting Better places to live | ||
| Quiz on Global Cost of Living | ||
| 15. | Capstone Project 5: Customer Segmentation Engine (28 minutes) | Understanding the Problem Statement |
| Setting up the Environment | ||
| Data Analysis and Visualization | ||
| KMeans Clustering Analysis | ||
| Major Learnings from the projects | ||
| Quiz on Customer Segmentation Engine | ||
| 16. | Outro Section (02 minutes) | Conclusion |
| How to Get Your Certificate of Completion | ||
| 17. | Bonus Section (01 minute) | Bonus Lecture |
Resources Required
No prior knowledge is required.
Featured Review
Sanket Bhoi (5/5): This course is the best option for me because I have always been looking to do a course in Data Science with Python. This course is well structured and will make you understand data science step by step. I highly recommend this course for those who want to learn data science from basics to advanced.
Pros
- Joy D Moyo (5/5): This is an excellent course for those who are unfamiliar with Python.
- Gajam venu (4/5): The concepts that I want are up to the mark teaching style is excellent looking forward to upcoming lectures
- Fariya Ammara (5/5): Excellent course! The clarity of subjects and the presentations were awesome.
- Hana Kovac (5/5): This is an excellent program with an excellent explanation and knowledge-sharing course.
Cons
Raju Gupta (2/5): The Intro and outro music on each video was really unnecessary.
About the Author
The course is offered by Data is Good Academy. They are a team of Google, Facebook, and Kaggle Grandmasters. With a 4.3 instructor rating and 7,986 reviews on Udemy, they offer 31 courses and have taught 253,886 students so far.
- They are a bootstrapped, indie tech education company.
- Their mission is to transform education such that it is available to everyone and is both inexpensive and accessible.
- Their sole focus is to provide everyone on the planet with access to top-notch technical education.
- They are on a mission to develop a curriculum that will inspire the students to learn a subject, fall in love with it, and pursue it with a passion for the rest of their lives.
Comparison Table
| Parameters | Data Science Bootcamp 2022: 5 Data Science Projects | Machine Learning Real World projects in Python | Machine Learning & Data Science A-Z: Hands-on Python 2022 |
|---|---|---|---|
| Offers | INR 799 | INR 455 ( | INR 455 ( |
| Duration | 15.5 hours | 13 hours | 14.5 hours |
| Rating | 4.3 /5 | 4.3 /5 | 4.6 /5 |
| Student Enrollments | 38,905 | 74,294 | 68,717 |
| Instructors | Data Is Good Academy | Shan Singh | Navid Shirzadi |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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