data science

“Data Science Training Course with Python for Data Analysis” helps the students to learn statistics, visualization, machine learning, and more with this comprehensive guide to practical data science with Python. This course provides a comprehensive manual on using Python for real data science. Therefore, if students take this course alone, they can avoid taking other courses or purchasing books on Python-based data science because it covers every facet of real data science.

Companies all over the world use Python to sort through the deluge of information at their disposal in this age of big data. Python may be used to store, filter, manage, and manipulate data, giving their business a competitive edge and catapulting your career to the next level. Python data science courses and books treat machine learning and data science as interchangeable concepts and fail to take into consideration the topic’s multidimensionality. Currently, udemy is offering the Data Science Training Course with Python for Data Analysis course for up to 87 % off i.e. INR 449 (INR 3,499).

Who can opt for this course?

  • Anyone who wants to learn Python-based practical data science
  • Anyone who is interested in learning how to use Python to implement machine learning algorithms
  • People looking to start using Python for deep learning
  • People looking to use Python to work with real-world data
  • Anyone looking to expand into data analysis who has experience with Python
  • Anyone who wants to learn how to use iPython to become proficient in exploratory data analysis, statistical modeling, and visualisation

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration12 Hours
Student Enrollment9,406 students
InstructorMinerva Singh
Topics Covered
  • Introduction to the Python Data Science Environment, Some Miscellaneous IPython Usage Facts
  • Matrix Arithmetic and Linear Systems
  • Basic Data Handling: Starting with Conditional Data Selection
Course LevelBeginner
Total Student Reviews1,757

Learning Outcomes

  • Install Anaconda and work in the iPython/Jupyter environment for Python data analytics, a robust framework for data science analysis
  • Learn How To Use The Most Popular Python Data Science Packages, Such As Numpy, Pandas, Scikit-Learn, and Matplotlib
  • Be able to read data from many sources, including data from websites, and clean the data using data analysis techniques
  • Utilize Python to do data exploration and pre-processing tasks including tabulation, pivoting, and data summarization
  • Develop Your Skills Working With Real-World Data Collected From Various Sources
  • Perform Data Visualization and learn when to use which techniques
  • Use Python To Perform The Most Popular Statistical Data Analysis Techniques, Such As T-Tests & Linear Regression
  • Recognize The Difference Between Statistical Data Analysis & Machine Learning
  • Use real-world data and various unsupervised learning techniques
  • Use supervised learning techniques on actual data, including classification and regression
  • Review The Generality And Accuracy Of Machine Learning Models
  • Construct fundamental neural networks and deep learning algorithms
  • Implement Deep Neural Networks Using The Effective H2O Framework

Course Content

S.No.Module (Duration)Topics
1.Introduction to the Data Science in Python Bootcamp (01 hour 00 minutes)What is Data Science?
Introduction to the Course & Instructor
Data For the Course
Introduction to the Python Data Science Tool
For Mac Users
Introduction to the Python Data Science Environment
Some Miscellaneous IPython Usage Facts
Online iPython Interpreter
Conclusion to Section 1
2.Introduction to Python Pre-Requisites for Data Science (12 minutes)Rationale Behind This Section
Different Types of Data Used in Statistical & ML Analysis
Different Types of Data are Used Programatically
Python Data Science Packages To Be Used
Conclusions to Section 2
3.Introduction to Numpy (01 hour 10 minutes)Numpy: Introduction
Create Numpy Arrays
Numpy Operations
Matrix Arithmetic and Linear Systems
Numpy for Basic Vector Arithmetic
Numpy for Basic Matrix Arithmetic
Broadcasting with Numpy
Solve Equations with Numpy
Numpy for Statistical Operation
Conclusion to Section 3
Section 3 Quiz
4.Introduction to Pandas (40 minutes)Data Structures in Python
Read in Data
Read in CSV Data Using Pandas
Read in Excel Data Using Pandas
Reading in JSON Data
Read in HTML Data
Conclusion to Section 4
5.Data Pre-Processing/Wrangling (01 hour 36 minutes)The rationale behind this section
Removing NAs/No Values From Our Data
Basic Data Handling: Starting with Conditional Data Selection
Drop Column/Row
Subset and Index Data
Basic Data Grouping Based on Qualitative Attributes
Rank and Sort Data
Merging and Joining Data Frames
Conclusion to Section 5
6.Introduction to Data Visualizations (01 hour 29 minutes)What is Data Visualization?
Some Theoretical Principles Behind Data Visualization
Histograms-Visualize the Distribution of Continuous Numerical Variables
Boxplots-Visualize the Distribution of Continuous Numerical Variables
Scatter Plot-Visualize the Relationship Between 2 Continuous Variables
Pie Chart
Line Chart
Conclusions to Section 6
7.Statistical Data Analysis-Basic (01 hour 16 minutes)What is Statistical Data Analysis?
Some Pointers on Collecting Data for Statistical Studies
Some Pointers on Exploring Quantitative Data
Explore the Quantitative Data: Descriptive Statistics
Grouping & Summarizing Data by Categories
Visualize Descriptive Statistics-Boxplots
Common Terms Relating to Descriptive Statistics
Data Distribution- Normal Distribution
Check for Normal Distribution
Standard Normal Distribution and Z-scores
Confidence Interval-Theory
Confidence Interval-Calculation
Conclusions to Section 7
8.Statistical Inference & Relationship Between Variables (01 hour 35 minutes)What is Hypothesis Testing?
Test the Difference Between the Two Groups
Test the Difference Between More Than Two Groups
Explore the Relationship Between Two Quantitative Variables
Correlation Analysis
Linear Regression-Theory
Linear Regression-Implementation in Python
Conditions of Linear Regression
Conditions of Linear Regression-Check in Python
Polynomial Regression
GLM: Generalized Linear Model
Logistic Regression
Conclusions to Section 8
Section 8 Quiz
9.Machine Learning for Data Science (11 minutes)How is Machine Learning Different from Statistical Data Analysis?
What is Machine Learning (ML) About? Some Theoretical Pointers
10.Unsupervised Learning in Python (48 minutes)Unsupervised Classification- Some Basic Ideas
KMeans-implementation on the iris data
Quantifying KMeans Clustering Performance
KMeans Clustering with Real Data
How Do We Select the Number of Clusters?
Hierarchical Clustering-theory
Hierarchical Clustering-practical
Principal Component Analysis (PCA)-Theory
Principal Component Analysis (PCA)-Practical Implementation
Conclusions to Section 10
11.Supervised Learning (01 hour 38 minutes)What is This Section About?
Data Preparation for Supervised Learning
Pointers on Evaluating the Accuracy of Classification and Regression Modelling
Using Logistic Regression as a Classification Model
SVM- Linear Classification
SVM- Non-Linear Classification
Support Vector Regression
Gradient Boosting-classification
Gradient Boosting-regression
Voting Classifier
Conclusions to Section 11
Section 11 Quiz
12.Artificial Neural Networks (ANN) and Deep Learning (DL) (50 minutes)Theory Behind ANN and DNN
Perceptrons for Binary Classification
Getting Started with ANN-binary classification
Multi-label classification with MLP
Regression with MLP
MLP with PCA on a Large Dataset
Start With Deep Neural Network (DNN)
Start with H20
Default H2O Deep Learning Algorithm
Specify the Activation Function
H2O Deep Learning For Predictions
Conclusions to Section 12
Section 12 Quiz
13.Miscellaneous Lectures & Information (25 minutes)Data For This Section
Read in Data from Online CSV
Read Data from a Database
Data Imputation
Accessing GitHub

Resources Required

  • possess basic computer skills, including the ability to install programs
  • An interest in learning data science
  • Prior Python experience is helpful but not required

Featured Review

SR Reddi (5/5): It has been a superb learning experience going through the lectures of this course. it is a very valuable course which contains the latest information on the subject. It has immense opportunities for practical application.


  • Hia Sumaiya (5/5): Best course materials I have ever seen! I have struggled to understand data analysis using python, and I almost gave up.
  • Pavel Hoq (5/5): Best course ever! Details explained theory, exclusive resources, and well-decorated session makes this course awesome!
  • Shikhar Agrawal (3/5): Any course needs practice assignments so that it can be understood perfectly.
  • Yapsi Minsa (5/5): One of the best courses on these topics that I’ve taken till now.


  • Vader S (2/5) : UPDATE: After a short pause and asking some pointed questions about some concepts I was disappointed in the engagement and delivery of the instructor.
  • Srinivas Raghupatruni (1/5): This is not a good course for people who want to make a career in data science.
  • Srinivas Raghupatruni (1/5): The instructor doesn’t explain the concepts but shows already written code and reads line by line which is not good.
  • Abhijay Mishra (1/5): I have learned from some bad teachers but those teachers couldn’t teach properly.

About the Author

The instructor of this course is Minerva Singh who is a Bestselling Instructor & Data Scientist(Cambridge Uni) with a 4.4 instructor rating and 17,641 reviews on Udemy. Minerva Singh offers 49 Courses and has taught 89,647 Students so far.

  • Minerva Singh finished her Ph.D. in 2017 at the University of Cambridge in the UK, where she concentrated on using data science tools to calculate the effects of forest loss on tropical ecosystems
  • Minerva Singh graduated from Oxford University with an MPhil in the School of Geography and Environment and an MSc in the Department of Engineering
  • Minerva Singh have more than ten years of experience conducting academic research (published in prestigious international peer-reviewed scientific journals like PLOS One) and offering advice to non-governmental and business stakeholders on matters relating to data science, deep learning, and earth observation (EO)
  • Minerva Singh have a proven track record of using R and Python to accomplish machine learning, data visualization, spatial data analysis, deep learning, and NLP applications
  • Minerva Singh has developed my statistical and data analysis skills through a variety of MOOCs, including The Analytics Edge (an R-based statistics and machine learning course offered by EdX), Statistical Learning (an R-based Machine Learning course offered by Stanford online), and the IBM Data Science Professional Certificate Track
  • Minerva Singh has also received my education from some of the best universities in the world
  • Minerva Singh have a wide range of specialties, including deep learning (Tensorflow, Keras), machine learning, spatial data analysis (including processing EO data), data visualization, natural language processing, and financial analysis, among others
  • Minerva Singh has served as a peer reviewer for esteemed academic publications like Remote Sensing and delivered guest talks at illustrious gatherings like the Open Data Science Conference (ODSC)

Comparison Table

ParametersComplete Data Science Training with Python for Data AnalysisData Science: Natural Language Processing (NLP) in PythonNatural Language Processing with Deep Learning in Python
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration13 hours12 hours12 hours
Rating4.3 /54.6 /54.6 /5
Student Enrollments9,40643,19942,420
InstructorsMinerva SinghLazy Programmer Inc.Lazy Programmer Inc.
Register HereApply Now!Apply Now!Apply Now!

Leave feedback about this

  • Rating