“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 Pythonbased 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 Pythonbased 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 realworld 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 Highlights  Details 

Registration Link  Apply Now! 
Price  INR 449 ( 
Duration  12 Hours 
Rating  4.3/5 
Student Enrollment  9,406 students 
Instructor  Minerva Singh https://www.linkedin.com/in/minervasingh 
Topics Covered 

Course Level  Beginner 
Total Student Reviews  1,757 
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, ScikitLearn, 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 preprocessing tasks including tabulation, pivoting, and data summarization
 Develop Your Skills Working With RealWorld 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 TTests & Linear Regression
 Recognize The Difference Between Statistical Data Analysis & Machine Learning
 Use realworld 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 PreRequisites 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 PreProcessing/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  
Crosstabulation  
Reshaping  
Pivoting  
Rank and Sort Data  
Concatenate  
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  
HistogramsVisualize the Distribution of Continuous Numerical Variables  
BoxplotsVisualize the Distribution of Continuous Numerical Variables  
Scatter PlotVisualize the Relationship Between 2 Continuous Variables  
Barplot  
Pie Chart  
Line Chart  
Conclusions to Section 6  
7.  Statistical Data AnalysisBasic (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 StatisticsBoxplots  
Common Terms Relating to Descriptive Statistics  
Data Distribution Normal Distribution  
Check for Normal Distribution  
Standard Normal Distribution and Zscores  
Confidence IntervalTheory  
Confidence IntervalCalculation  
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 RegressionTheory  
Linear RegressionImplementation in Python  
Conditions of Linear Regression  
Conditions of Linear RegressionCheck 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 
KMeanstheory  
KMeansimplementation on the iris data  
Quantifying KMeans Clustering Performance  
KMeans Clustering with Real Data  
How Do We Select the Number of Clusters?  
Hierarchical Clusteringtheory  
Hierarchical Clusteringpractical  
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  
RFClassification  
RFRegression  
SVM Linear Classification  
SVM NonLinear Classification  
Support Vector Regression  
knnClassification  
knnRegression  
Gradient Boostingclassification  
Gradient Boostingregression  
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 ANNbinary classification  
Multilabel 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.
Pros
 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 welldecorated 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.
Cons
 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 peerreviewed scientific journals like PLOS One) and offering advice to nongovernmental 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 Rbased statistics and machine learning course offered by EdX), Statistical Learning (an Rbased 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
Parameters  Complete Data Science Training with Python for Data Analysis  Data Science: Natural Language Processing (NLP) in Python  Natural Language Processing with Deep Learning in Python 

Offers  INR 455 (  INR 455 (  INR 455 ( 
Duration  13 hours  12 hours  12 hours 
Rating  4.3 /5  4.6 /5  4.6 /5 
Student Enrollments  9,406  43,199  42,420 
Instructors  Minerva Singh  Lazy Programmer Inc.  Lazy Programmer Inc. 
Register Here  Apply Now!  Apply Now!  Apply Now! 
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