“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, scikitlearn, 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, scikitlearn, Pandas, matplotlib, Seaborn, Python programming with NumPy, Advanced Statistical Analysis, Tableau, Deep Learning with TensorFlow.
Learning Outcomes
 Understanding mathematics behind Machine Learning
 Learn indemand data science skills such as Statistical Analysis, Python programming with NumPy, Pandas, matplotlib, and Seaborn, Tableau, Machine Learning with Statsmodels, and scikitlearn, 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; Zscore 
Confidence Interval Clarifications  
Confidence Intervals; Population Variance Unknown; Tscore  
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  
pvalue  
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  
Builtin 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)  ObjectOriented 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 RSquared  
Test for Significance of the Model (FTest)  
OLS Assumptions  
Dealing with Categorical Data – Dummy Variables  
28  Linear Regression with Sklearn (54 Minutes)  Calculating the Adjusted RSquared in sklearn 
Feature Selection (Fregression)  
Creating a Summary Table with Pvalues  
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  KMeans clustering (49 Minutes)  Clustering Categorical Data 
How to Choose the Number of Clusters  
Pros and Cons of KMeans 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 AZ™: HandsOn 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! 
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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 bigtime 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 welldetailed 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 realworld 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 scikitlearn, 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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