“The Data Science Course 2023: Complete Data Science Bootcamp” is a complete guide on machine learning and data science. It dives deep into various data science and machine learning tools such as NumPy, Statsmodels, sci-kit-learn, etc. The course is 29.5 hours long having 64 modules and 488 lectures. This course is available at 87% off i.e. INR 455 (INR 3,499).
“The Data Science Course 2023: Complete Data Science Bootcamp” helps beginners to learn data science skills such as Statistical Analysis, Machine Learning with Statsmodels, scikit-learn, Pandas, matplotlib, Seaborn, Python programming with NumPy, Advanced Statistical Analysis, Tableau, Deep Learning with TensorFlow.
Learning Outcomes
- Understanding mathematics behind Machine Learning
- Learn in-demand data science skills such as Statistical Analysis, Python programming with NumPy, Pandas, matplotlib, and Seaborn, Tableau, Machine Learning with Statsmodels, and scikit-learn, Deep Learning with TensorFlow.
- Understanding linear and logistic regressions in Python
- Learn cluster and factor analysis.
Course Highlights
| Key Highlights | Details |
|---|---|
| Course Name | The Data Science Course 2023: Complete Data Science Bootcamp |
| Duration | 29.5 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 4.86 lakhs |
| Instructor | 365 Careers and 365 Careers Team |
| Course Level (Resources Required) | Beginner |
| Coding Exercises | No |
| Projects | No |
| Total Student Reviews | 1.06 lakhs |
| Merits |
|
| Shortcomings |
|
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1 | Part 1: Introduction in the Field of Data Science (19 Minutes) | – |
| 2 | The various Data Science Disciplines (31 Minutes) | What is the difference between Analysis and Analytics |
| 3 | Connecting the Data Science Disciplines (7 Minutes) | Applying Traditional Data, Big Data, BI, Traditional Data Science and ML |
| 3 | Benefits of Each Discipline (5 Minutes) | The Reason Behind These Disciplines |
| 4 | Popular Data Science Techniques (54 Minutes) | Techniques for Working with Traditional Data |
| Techniques for Working with Big Data | ||
| Business Intelligence (BI) Techniques | ||
| Techniques for Working with Traditional Methods | ||
| Machine Learning (ML) Techniques | ||
| Types of Machine Learning | ||
| 5 | Popular Data Science Tools (6 Minutes) | Necessary Programming Languages and Software Used in Data Science |
| 6 | Careers in Data Science (3 Minutes) | Finding the Job – What to Expect and What to Look for |
| 7 | Debunking Common Misconceptions (4 Minutes) | – |
| 8 | Part 2: Probability (23 Minutes) | – |
| 9 | Combinatorics (43 Minutes) | Permutations and How to Use Them |
| Simple Operations with Factorials | ||
| Solving Variations with Repetition | ||
| Solving Variations without Repetition | ||
| Solving Combinations | ||
| Symmetry of Combinations | ||
| Solving Combinations with Separate Sample Spaces | ||
| 8 | Bayesian Inference (55 Minutes) | Ways Sets Can Interact |
| Intersection of Sets | ||
| Mutually Exclusive Sets | ||
| The Law of Total Probability | ||
| The Multiplication Law | ||
| Bayes’ Law | ||
| 9 | Distributions (1 Hr 17 Minutes) | Fundamentals of Probability Distributions |
| Types of Probability Distributions | ||
| Characteristics of Discrete Distributions | ||
| Characteristics of Continuous Distributions | ||
| 10 | Probability in Other Fields (19 Minutes) | Probability in Finance |
| Probability in Statistics | ||
| Probability in Data Science | ||
| 11 | Part 3: Statistics (4 Minutes) | – |
| 12 | Descriptive Statistics (48 Minutes) | Types of Data |
| Levels of Measurement | ||
| Mean, median and mode | ||
| Skewness | ||
| Standard Deviation and Coefficient of Variation | ||
| Covariance | ||
| Correlation Coefficient | ||
| 13 | Inferential Statistics Fundamentals (22 Minutes) | What is a Distribution |
| The Normal Distribution | ||
| The Standard Normal Distribution | ||
| Central Limit Theorem | ||
| Standard error | ||
| Estimators and Estimates | ||
| 14 | Inferential Statistics: Confidence Intervals (44 Minutes) | Confidence Intervals; Population Variance Known; Z-score |
| Confidence Interval Clarifications | ||
| Confidence Intervals; Population Variance Unknown; T-score | ||
| Margin of Error | ||
| Confidence intervals. Two means. Dependent samples | ||
| 15 | Hypothesis Testing (48 Minutes) | Rejection Region and Significance Level |
| Type I Error and Type II Error | ||
| Test for the Mean. Population Variance Known | ||
| p-value | ||
| Test for the Mean. Population Variance Unknown | ||
| Test for the Mean. Dependent Samples | ||
| 16 | Part 4: Introduction to Python (30 Minutes) | – |
| 17 | Variables and Data Types (12 Minutes) | Variables |
| Numbers and Boolean Values in Python | ||
| Python Strings | ||
| 18 | Basic Python Syntax (11 Minutes) | Using Arithmetic Operators in Python |
| The Double Equality Sign | ||
| How to Reassign Values | ||
| 19 | Other Python Operators (8 Minutes) | Comparison Operators |
| Logical and Identity Operators | ||
| 20 | Conditional Statements (14 Minutes) | The IF Statement |
| The ELSE Statement | ||
| The ELIF Statement | ||
| A Note on Boolean Values | ||
| 21 | Python Functions (19 Minutes) | Defining a Function in Python |
| How to Create a Function with a Parameter | ||
| Defining a Function in Python – Part II | ||
| Conditional Statements and Functions | ||
| Built-in Functions in Python | ||
| 22 | Sequences (35 Minutes) | Lists |
| Using Methods | ||
| List Slicing | ||
| Tuples | ||
| 23 | Iterations (33 Minutes) | For Loops |
| While Loops and Incrementing | ||
| Lists with the range() Function | ||
| Conditional Statements and Loops | ||
| 24 | Advanced Python Tools (13 Minutes) | Object-Oriented Programming |
| Modules and Packages | ||
| What is the Standard Library? | ||
| Importing Modules in Python | ||
| 25 | Part 5: Advanced Statistical Methods in Python (1 Minute) | – |
| 26 | Linear Regression with Statsmodel (41 Minutes) | The Linear Regression Model |
| Correlation vs Regression | ||
| Geometrical Representation of the Linear Regression Model | ||
| Python Packages Installation | ||
| How to Interpret the Regression Table | ||
| 27 | Multiple Linear Regression with Statsmodel (42 Minutes) | Multiple Linear Regression |
| Adjusted R-Squared | ||
| Test for Significance of the Model (F-Test) | ||
| OLS Assumptions | ||
| Dealing with Categorical Data – Dummy Variables | ||
| 28 | Linear Regression with Sklearn (54 Minutes) | Calculating the Adjusted R-Squared in sklearn |
| Feature Selection (F-regression) | ||
| Creating a Summary Table with P-values | ||
| Feature Selection through Standardization of Weights | ||
| 29 | Logistic Regression (41 Minutes) | A Simple Example in Python |
| Logistic vs Logit Function | ||
| Building a Logistic Regression | ||
| An Invaluable Coding Tip | ||
| What do the Odds Actually Mean | ||
| Binary Predictors in a Logistic Regression | ||
| 30 | Cluster Analysis (14 Minutes) | Difference between Classification and Clustering |
| Math Prerequisites | ||
| 31 | K-Means clustering (49 Minutes) | Clustering Categorical Data |
| How to Choose the Number of Clusters | ||
| Pros and Cons of K-Means Clustering | ||
| To Standardize or not to Standardize | ||
| Market Segmentation with Cluster Analysis | ||
| 32 | Part 6: Mathematics (51 Minutes) | What is a Matrix? |
| Scalars and Vectors | ||
| Linear Algebra and Geometry | ||
| Arrays in Python – A Convenient Way To Represent Matrices | ||
| What is a Tensor? | ||
| Addition and Subtraction of Matrices | ||
| 33 | Part 7: Deep Learning (3 Minutes) | – |
| 34 | Introduction to Neural Networks (43 Minutes) | Training the Model |
| Types of Machine Learning | ||
| The Linear Model | ||
| Graphical Representation of Simple Neural Networks | ||
| What is the Objective Function? | ||
| 35 | How to Build Neural Networks from scratch with NumPy (21 Minutes) | – |
| 36 | TensorFlow 2.0 (28 Minutes) | How to Install TensorFlow 2.0 |
| TensorFlow Outline and Comparison with Other Libraries | ||
| Outlining the Model with TensorFlow 2 | ||
| 37 | Overfitting (20 Minutes) | – |
| 38 | Initialization (8 Minutes) | – |
| 39 | Preprocessing (15 Minutes) | – |
Resources Required
- Anaconda will be installed through the guidance of the course provider
- Microsoft Excel 2003, 2010, 2013, 2016, or 365
Comparison Table
| Parameters | The Data Science Course 2023: Complete Data Science Bootcamp | Machine Learning A-Z™: Hands-On Python & R In Data Science | The Complete SQL Bootcamp 2023: Go from Zero to Hero |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 29.5 Hours | 44 Hours | 9 Hours |
| Rating | 4.6 | 4.5 | 4.7 |
| Student Enrollments | 4.86 lakhs | 8.65 lakhs | 4.99 lakhs |
| Instructors | 365 Careers, 365 Careers Team | Kirill Eremenko, Hadelin de Ponteves, Ligency Team | Jose Portilla |
| Level | Beginner | Beginner | Beginner |
| Topics Covered | Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning | Machine Learning with Python and R | SQL |
| Coding Exercises | No | No | No |
| Projects | No | No | No |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
| Compare Similar Courses | 2023 Complete Python Bootcamp from Zero to Hero in Python | Udemy The Complete JavaScript Course 2023 from Zero to Expert | Learn Ethical Hacking From Scratch |
| Mastering Data Structures & Algorithms using C and C++ | Build Responsive Real-World Websites with HTML and CSS | The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert |
Student Reviews
- Gaurav K. (5.0/5): “Though the course is of 30 hours but if you are a complete beginner and you try to do all the exercises honestly and by yourself or in your way. It will take around 2 months at least to do the whole course and all the problems. For me at least it did.”
- Titilayo A. (5.0/5): “I am greatly impressed with the content. I like the fact that there are exercises that you can practice. It is well explained.”
- Rico D. (5.0/5): “Learned so much from this course! I am a little regretful that I didn’t purchase the course earlier and relied much on tutorial videos regarding data science. It could have saved much of my time learning. The course is well structured and will let you understand data science step by step.”
- Chinedum O. (5.0/5): “Wow, I thought I had knowledge of what data science is all about not until I met this course, this is really exposure of what data science is all about. I am really grateful, you guys really brought out this big-time making sure every hint is understood. I appreciate and would like everybody to join us on this marvelous course.”
- Itai M (5.0/5): “My skills have improved immensely through the well-detailed explanations throughout the course. This was well done, thank you.”
- Tobi O (4.0/5): “So far so good. I wished they would go through some of the syntaxes for the modules before we just started using them. I think that would provide some context for when we do use them. It might also help to slow down on some of the more complex topics e.g. the OLS assumptions.”
- James C (4.0/5): “Awesome course, only to nits: The beginning material has some gaps, but if something doesn’t make sense keep going, many topics are revisited and in greater detail. There are some parts in the exercises that need to be updated, however, they are usually covered well in that section’s Q&A.”
- Michela C (4.0/5): “I have enjoyed this course in general. Personally, I did not like the statistics sections. I believe a little bit more practical examples would have helped the learning process. For example, I have followed a nice course that was explaining statistics and math while showing real-world cases. Of course, depending on the student certain approaches work better, but I am satisfied! Thank you very much for your work.”
- Frank F (4.0/5): “Starts off quite slow and some of the maths theory is missing but so far concepts are explained well. Some of the quizzes are not clear what the question is asking.”
- Lim X (3.0/5): “The front part of the background was good and clear. However, when we reach the part about applying, the instructor tends to skip some parts of the code without explaining or relating clearly to the background. Some of the codes provided also had problems and Q&A solutions did not fix them. Overall, still an informative course on data science.”
- Okot P (3.0/5): “The statistics instructor assumes knowledge of certain concepts among the students and this generates unnecessary questions in the Q&A section. I’m ok with googling the concepts but this doesn’t save time and also leads to diversion of attention & unwanted breaks.”
The Data Science Course 2023: Complete Data Science Bootcamp: FAQs
Ques. What is the fee for the course?
Ans. The price of the course is INR 3499 but the discounted price is INR 455.
Ques. What will I learn in the course?
Ans. Students will learn all about data science and machine learning along with various skills such as Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and sci-kit-learn, Deep learning with TensorFlow.
Ques. What is the duration of the course?
Ans. The duration of the course is 29.5 hours.
Ques. Is there a certification from Udemy?
Ans. Udemy will provide a certificate of completion after the student completes the course.
Ques. What is the rating?
Ans. The course has 4.6 rating provided by 1,06,280 students.
Ques. Do I have lifetime access to this course?
Ans. All students have lifetime access to this course.
Ques. Can I access the course on mobile devices, laptops, and TV?
Ans. The course is available in mobile and TV.
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