“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 HighlightsDetails
Course NameThe Data Science Course 2023: Complete Data Science Bootcamp
Duration29.5 Hours
Student Enrollment4.86 lakhs
Instructor365 Careers and 365 Careers Team
Course Level (Resources Required)Beginner
Coding ExercisesNo
Total Student Reviews1.06 lakhs
  • Understanding the mathematics behind machine learning
  • Learn various tools for machine learning through great visualizations.
  • The subject material is extremely detailed
  • The deep dive into mathematics might be a little overwhelming
  • Certain knowledge in statistics is assumed which generates confusion.

Course Content

S.No.Module (Duration)Topics
1Part 1: Introduction in the Field of Data Science (19 Minutes)
2The various Data Science Disciplines (31 Minutes)What is the difference between Analysis and Analytics
3Connecting the Data Science Disciplines (7 Minutes)Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
3Benefits of Each Discipline (5 Minutes)The Reason Behind These Disciplines
4Popular 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
5Popular Data Science Tools (6 Minutes)Necessary Programming Languages and Software Used in Data Science
6Careers in Data Science (3 Minutes)Finding the Job – What to Expect and What to Look for
7Debunking Common Misconceptions (4 Minutes)
8Part 2: Probability (23 Minutes)
9Combinatorics (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
8Bayesian Inference (55 Minutes)Ways Sets Can Interact
Intersection of Sets
Mutually Exclusive Sets
The Law of Total Probability
The Multiplication Law
Bayes’ Law
9Distributions (1 Hr 17 Minutes)Fundamentals of Probability Distributions
Types of Probability Distributions
Characteristics of Discrete Distributions
Characteristics of Continuous Distributions
10Probability in Other Fields (19 Minutes)Probability in Finance
Probability in Statistics
Probability in Data Science
11Part 3: Statistics (4 Minutes)
12Descriptive Statistics (48 Minutes)Types of Data
Levels of Measurement
Mean, median and mode
Standard Deviation and Coefficient of Variation
Correlation Coefficient
13Inferential Statistics Fundamentals (22 Minutes)What is a Distribution
The Normal Distribution
The Standard Normal Distribution
Central Limit Theorem
Standard error
Estimators and Estimates
14Inferential 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
15Hypothesis Testing (48 Minutes)Rejection Region and Significance Level
Type I Error and Type II Error
Test for the Mean. Population Variance Known
Test for the Mean. Population Variance Unknown
Test for the Mean. Dependent Samples
16Part 4: Introduction to Python (30 Minutes)
17Variables and Data Types (12 Minutes)Variables
Numbers and Boolean Values in Python
Python Strings
18Basic Python Syntax (11 Minutes)Using Arithmetic Operators in Python
The Double Equality Sign
How to Reassign Values
19Other Python Operators (8 Minutes)Comparison Operators
Logical and Identity Operators
20Conditional Statements (14 Minutes)The IF Statement
The ELSE Statement
The ELIF Statement
A Note on Boolean Values
21Python 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
22Sequences (35 Minutes)Lists
Using Methods
List Slicing
23Iterations (33 Minutes)For Loops
While Loops and Incrementing
Lists with the range() Function
Conditional Statements and Loops
24Advanced Python Tools (13 Minutes)Object-Oriented Programming
Modules and Packages
What is the Standard Library?
Importing Modules in Python
25Part 5: Advanced Statistical Methods in Python (1 Minute)
26Linear 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
27Multiple 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
28Linear 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
29Logistic 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
30Cluster Analysis (14 Minutes)Difference between Classification and Clustering
Math Prerequisites
31K-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
32Part 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
33Part 7: Deep Learning (3 Minutes)
34Introduction 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?
35How to Build Neural Networks from scratch with NumPy (21 Minutes)
36TensorFlow 2.0 (28 Minutes)How to Install TensorFlow 2.0
TensorFlow Outline and Comparison with Other Libraries
Outlining the Model with TensorFlow 2
37Overfitting (20 Minutes)
38Initialization (8 Minutes)
39Preprocessing (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

ParametersThe Data Science Course 2023: Complete Data Science BootcampMachine Learning A-Z™: Hands-On Python & R In Data ScienceThe Complete SQL Bootcamp 2023: Go from Zero to Hero
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration29.5 Hours44 Hours9 Hours
Student Enrollments4.86 lakhs8.65 lakhs4.99 lakhs
Instructors365 Careers, 365 Careers TeamKirill Eremenko, Hadelin de Ponteves, Ligency TeamJose Portilla
Topics CoveredMathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep LearningMachine Learning with Python and RSQL
Coding ExercisesNoNoNo
Register HereApply 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 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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