Home Data Science with Python Certification Training Course

Data Science with Python Certification Training Course

With a 100% Job Assistance

Get your data science career in motion with Python for the Data Science course. Prepzee offers a course that helps you gain proficiency in programming concepts of Python, such as file operations, data operations, and other Python libraries like Matplotlib, Pandas, and Numpy, which help in data manipulation, analysis, and visualization. The Data Science with Python course is in accordance with the standards of the industry and aids in gaining an understanding of Recommendation Systems, Machine Learning, and other specters of Data Science.   

10% Early bid discount
Enroll Now
  • 4.9



  • 4.9

    mentors rating
  • 4.9




93% of our students get employed

Join the course and follow their successful careers!

Key Features

100% Money Back Guarantee
  • image36 Hrs Instructor-Led Training
  • image40 Hrs Self-paced Videos
  • image32 Hrs Project & Exercises
  • imageNASSCOM CertificationLifetime Free Upgrade
  • imageJob Assistance
  • imageFlexible Schedule
  • imageLifetime Free Upgrade
  • imageMentor Support

About Profession

A python is an essential tool for implementing data science applications.

Data Visualisation
Machine Learning and its algorithms
Time Series analysis

Future of the Data Science with Python course

  • Up to 59% average salary hike
    Highest Salary Offered
  • 40 LPA Highest salary
    Highest Salary Offered

What You will Learn?

  • image
    Introduction to Cloud Computing and Python

    The Python for Data Science training course begins with an introduction to cloud computing and Python. Understand Flow Control and Command-Line and discuss Python scrips on Windows/Unix. Learn about basic programming in Python and different applications and companies where Python is used.

  • image
    OOPs, Functions, Modules, Errors, and Exceptions

    Learn about object-oriented concepts in this module. Get a detailed understanding of standard libraries, Python functions and Parameters, Lambda functions-their features and syntax, and Modules used in Python. Learn how to handle errors and exceptions.

  • image
    Time Series Analysis

    Get complete knowledge of what Time Series Analysis (TSA) is. The module gets into the detail of the components and significance of TSA. Understand how to use TSA in Python. Learn how to implement Dicky-Fuller Test and understand how to convert non-stationary data to stationary.

  • image
    Data Science with Python Certification

    The curriculum of the course is designed by industry professionals keeping in mind the real-world requirements of the corporate world. Other than general guidelines, you will have help with preparing a resume, potential interview questions, mock interviews, and a reliable certification to go with it.

Skills Covered, Tool Covered

Tools Covered?

Placement Overview

  • 8.4 Lakhs
    Average CTC offered
  • 9 Days
    Placement time
  • Upto 350%
    Salary hike

Career Transition

Data Science with Python course is for you if:

We will assign you a top Expert from below

View our Expert currently online

Neerav Jain

Amazon Authorised Instructor
4.9 Average Rating

12+ Years in Cloud

Microsoft, Adobe & AWS certified young and dynamic professional with 14+ years of industry experience. Trained 8200+ professionals, and counting, on all cloud technologies including AWS, Azure and GCP, DevOps, AEM, iOS, UI/UX, Digital Marketing & advanced Front End and Backend development technologies.

Sidharth Kanan

Microsoft Certified Trainer
Average Rating

As a Data Engineer, I have 15+ yrs experience working on different cloud environments on various projects.I currently lead multi-work stream projects as a Cloud analyst and DevOps Engineer. Technologies I work on AWS, AZURE,Git hub, Jenkins, Ansible, Docker, Kubernetes ,Python ,Data Science On Azure and many more.

Manohar G

Manager at HP
Average Rating

Engaging, understanding, and knowledgeable technical trainer with over 14+ years of experience in a variety of software programs and technologies including Data Science, Artificial Intelligence, Data Engineering, Business Intelligence AWS, Azure and GCP, DevOps, AEM, iOS, UI/UX.

Pratik Kheria

Microsoft Certified Trainer
Average Rating

Always learning and always yearning with experience of more than 10+ yrs, have delivered more than 200+ mentoring/ training sessions online on Microsoft Azure, Amazon web services, Cyber Security, Python, Tableau and Power BI to more than 20000 students.

Python for Data Science Curriculum

  • image
    36 Hrs Instructor-Led Training
  • image
    24 Projects Industry Cases
  • image
    1 - 1 Placement mentoring
  • image
    Participate in monthly contests

1.1: Introduction to Cloud Computing 

1.2: Overview of Python

1.3: The Companies using Python

1.4: Discuss Python Scripts on UNIX/Windows

1.5: Different Applications where Python is Used

1.6: Conditional Statements

1.7: Values, Types, Variables

1.8: Loops

1.9: Writing to the Screen

1.10: Operands and Expressions

1.11: Command Line Arguments


  • Creating “Hello World” code
  • Demonstrating Loops
  • Demonstrating Conditional Statements
  • Variables


Download Brochure

2.1: Python files I/O Functions

2.2: Strings and their related operations

2.3: Lists and their related operations

2.4: Tuples and their related operations

2.5: Sets and their related operations

2.6: Dictionaries and their related operations

2.7: Numbers


  • Dictionary – properties, related operations
  • List – properties, related operations
  • Tuple – properties, related operations, compared with the list
  • Set – properties, related operations


Download Brochure

3.1: Functions

3.2: Function Parameters

3.3: Modules Used in Python

3.4: Module Search Path

3.5: Lambda Functions

3.6: Global Variables

3.7: Variable Scope and Returning Values

3.8: Object-Oriented Concepts

3.9: Standard Libraries

3.10: Package Installation Ways

3.11: The Import Statements

3.12: Errors and Exception Handling

3.13: Handling Multiple Exceptions


  • Lambda – Features, Syntax, Options, Compared with the Functions
  • Functions – Syntax, Arguments, Keyword Arguments, Return Values
  • Packages and Module – Modules, Import Options, sys Path
  • Sorting – Sequences, Dictionaries, Limitations of Sorting
  • Errors and Exceptions – Types of Issues, Remediation
Download Brochure

4.1: Data Analysis

4.2: Pandas – data structures & index operations

4.3: Reading and Writing data from Excel/CSV formats into Pandas

4.4: NumPy – arrays

4.5: Operations on arrays

4.6: Reading and writing arrays on files

4.7: Indexing, slicing and iterating

4.8: Matplotlib library

4.9: Metadata for imported Datasets

4.10: Types of plots – bar graphs, pie charts, histograms

4.11: Contour plots

4.12: Markers, colors, fonts, and styling

4.13: Grids, axes, plots


  • Pandas library- Creating series and data frames, Importing and exporting data
  • NumPy library- Creating NumPy array, operations performed on NumPy array
  • Matplotlib – Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot
Download Brochure

5.1: Basic Functionalities of a data object

5.2: Merging of Data objects

5.3: Concatenation of data objects

5.4: Types of Joins on data objects

5.5: Exploring a Dataset

5.6: Analysing a dataset


  • Pandas Function- itertuples(), std(), tail(), head(),Ndim(), axes(), values(), sum(), iteritems(), iterrows(), GroupBy operations, Aggregation, Concatenation, Merging and joining.
Download Brochure

6.1: Python Revision (NumPy, Pandas, scikit learn, matplotlib)

6.2: What is Machine Learning?

6.3: Machine Learning Categories

6.4: Machine Learning Process Flow

6.5: Machine Learning Use-Cases

6.6: Linear regression


  • Linear Regression – Boston Dataset


Download Brochure

7.1: What are Classification and its use cases?

7.2: What is a Decision Tree?

7.3: Creating a Perfect Decision Tree

7.4: Algorithm for Decision Tree Induction

7.5: Confusion Matrix

7.6: What is Random Forest?


  • Implementation of Logistic regression, Decision tree, Random forest


Download Brochure

8.1: Introduction to Dimensionality

8.2: Why Dimensionality Reduction

8.3: Factor Analysis

8.4: LDA

8.5: PCA

8.6: Scaling dimensional model


  • Scaling
  • PCA
Download Brochure

9.1: Illustrate how the Support Vector Machine works.

9.2: Hyperparameter Optimisation

9.3: What is a Support Vector Machine?

9.4: Implementation of Support Vector Machine for Classification

9.5: What is Naïve Bayes?

9.6: How Naïve Bayes works?

9.7: Implementing Naïve Bayes Classifier

9.8: Grid Search vs. Random Search


  • Implementation of SVM, Naïve Bayes.


Download Brochure

10.1: What is Hierarchical Clustering?

10.2: How does Hierarchical Clustering work?

10.3: What is K-means Clustering?

10.4: How does the K-means algorithm work?

10.5: How to do optimal clustering?

10.6: What is C-means Clustering?

10.7: What is Clustering & its Use Cases?


  • Implementing Hierarchical Clustering
  • Implementing K-means Clustering


Download Brochure

11.1: What are Association Rules?

11.2: Association Rule Parameters

11.3: Calculating Association Rule Parameters

11.4: Recommendation Engines

11.5: How do Recommendation Engines work?

11.6: Content-Based Filtering

11.7: Collaborative Filtering


  • Apriori Algorithm
  • Market Basket Analysis
Download Brochure

12.1: What is Reinforcement Learning?

12.2: Why Reinforcement Learning?

12.3: Elements of Reinforcement Learning

12.4: Epsilon Greedy Algorithm

12.5: Exploration vs. Exploitation dilemma

12.6: Q – Learning

12.7: Q values and V values

12.8: Values

12.9: Markov Decision Process (MDP)


  • Discounted Reward
  • Calculating Reward
  • Setting up an Optimal Action
  • Calculating Optimal quantities
  • Implementing Q Learning
Download Brochure

13.1: What is Time Series Analysis?

13.2: Components of TSA

13.3: Importance of TSA

13.4: MA model

13.5: White Noise

13.6: AR model

13.7: ARIMA model

13.8: ARMA model

13.9: ACF & PACF

13.10: Stationarity


  • Checking Stationarity
  • TSA Forecasting
  • Plot ACF and PACF
  • Generating the ARIMA plot
  • Converting non-stationary data to stationary
  • Implementing Dickey-Fuller Test
Download Brochure

14.1: What is Model Selection?

14.2: Need for Model Selection

14.3: What is Boosting?

14.4: Types of Boosting Algorithms

14.5: Adaptive Boosting

14.6: How do Boosting Algorithms work?

14.7: Cross–Validation


  • AdaBoost
  • Cross Validation


Download Brochure

15.1: What is Exploratory Data Analysis?

15.2: EDA Classification

15.3: EDA Techniques

15.4: Univariate Graphical EDA

15.5: Univariate Non-graphical EDA

15.6: Multivariate Graphical EDA

15.7: Multivariate Non-graphical EDA

15.8: Heat Maps


  • Implementing Non-Graphical EDA Techniques
  • Implementing Graphical EDA Techniques
Download Brochure

16.1: Data Visualisation

16.2: Business Intelligence tools

16.3: VizQL Technology

16.4: Connect to data from the Database

16.5: Connect to data from File

16.6: Chart Operations

16.7: Basic Charts

16.8: Calculations

16.9: Combining Data


  • Connecting to data from File, Databases, and Server
  • Performing operations on Hierarchies, Data Granularity, and Highlighting feature
  • Using basic functions for creating calculated fields
  • Performing User Input and What-if analysis
  • Creating Parameters
  • Defining LOD expressions


Download Brochure

17.1: Trend lines

17.2: Dashboards

17.3: Clustering

17.4: Reference lines

17.5: Forecasting

17.6: Using charts effectively

17.7: Geographic Maps

17.8: Publish to Tableau Online

17.9: Visual best practices

17.10: Story Points


  • Building Dashboard Layout and Formatting
  • Building Story points
  • Analyzing data using techniques including Forecasting, Trend Lines, Reference Lines, Clustering, and Geographic Maps


Download Brochure
View More

Free Career Counselling

We are happy to help you 24/7

    By providing your contact details, you agree to our Terms of Use & Privacy Policy

    Learn Projects & Assignments Handpicked byIndustry Leaders

    Our tutors are real business practitioners who hand-picked and created assignments and projects for you that you will encounter in real work.

    AWS Training Fees

    Corporate Training

    Train your Employees with Customized Learning
    Skill Passport
    • 300+ Hiring Partners
    • Create your profile & update your skills, Expertise
    • Get Noticed by our Hiring Partners & switch to the new job
    Job Portal
    • 1000+ Job Openings at present from various domains.
    • Daily New Job Postings by hiring partners
    • Select a Suitable job & Apply
    Resume Building
    • 200+ Sample Resumes to Refer
    • Free Download word File & Edit

    AWS Certification by Prepzee

    Prepzee’s AWS Solutions Architect Certificate Holders work at 1000s of companies like

    Get In Touch Get Sample Certificate


    Frequently Asked Questions

    Python is one of the most preferred programming languages worldwide and an excellent choice for anyone who aspires to make a career in Data Science. Prepzee offers a carefully crafted Python for Data Science course. With this training, you gain proficiency in various Python concepts, such as handling errors and exceptions, modules, and functions, and learn to use them to perform multiple operations in data science. You will learn from the curriculum designed by industry experts, work on assignments bit-by-bit, and work on real-time, highly relevant projects. The Data Science with Python course online training equips you to confidently apply for jobs in leading MNCs of the world at impressive pay. With Prepzee, you get lifetime access to course materials, videos, 24/7 support, and updated versions of the course, minus extra fees. It is undoubtedly a wise one-time investment.

    Prepzee offers 24/7 support to resolve queries. You simply raise the issue with the support team at any time. You can also opt for email assistance for all your requests. If not, a one-on-one session can also be arranged with the team. This session is, however, only provided for six months starting from your course date.

    All Data Scienec python experts at Prepzee have over twelve years of experience relevant to the industry. They are rightfully the experts on the subject matter, given that they have been actively working in the domain as consultants. You can check out the sample videos to ease your doubts.

    Prepzee provides active assistance for job placement to all candidates who have completed the training successfully. Additionally, we help candidates prepare for résumé and job interviews. We have exclusive tie-ups with reputed companies.

    Projects included in the training program are updated and hold high relevance and value in the real world. Projects help you apply the acquired learning in real-world industry structures. Training involves several projects that test practical knowledge, understanding, and skills. High-tech domains like e-commerce, networking, marketing, insurance, banking, sales, etc., make for the subjects of the projects you will work on. After completing the Projects, your skills will be synonymous with months of meticulous industry experience.

    Prepzee's Course Completion Certificate is awarded once the training program is completed, along with working on assignments, real-world projects, and quizzes, with a least 60 percent score in the qualifying exam.

    Actually, no. Our job assistance program intends to help you land the job of your dreams. The program offers opportunities to explore competitive vacancies in the corporates and look for a job that pays well and matches your profile and skill set. The final hiring decision will always be based on how you perform in the interview and the recruiter's requirements.