Data Science with Python Certification Course

Teaching Krow’s Data Science With Python Certification Course is strategically designed by the top industry experts to match the current market requirements and demands. The training course will effortlessly help you master the Python programming concepts like file operations, sequences, function loops, OOPS, conditional statements, modules & handling exceptions, libraries like NumPy, Matplotlib, Pandas, and many more. Throughout the training course offered by the Teaching Krow, you’ll be working on real-time projects, and this course prepares you to clear the certification exam on your first attempt.

Why should you take Data Science with Python Certification Course ?

Promotes faster development and processing and attracts higher paying packages.

Offers you to select the best company from the pool of opportunities available to you.

Offers a better understanding of Data Visualization and learn from the experts.


68 hours of blended learning
Interactive learning with Jupyter notebook labs
One year access
Mentor support
Projects and exercises

Data Science with Python Certification Course Overview

What Will You Learn In The Data Science With Python Certification Course by Teaching Krow?
  • Introduction to Python for Data Science
  • What is SQLite in Python? What are its Operations and Classes?
  • OOP Concepts, Expressions, and Functions
  • Creating Pig and Hive UDF in Python
  • Real-world Data Science Projects
  • Deploying Python for MapReduce Programming


Self Paced Training


Data Science with Python Certification Course Curriculum

Introduction to Python
  • Overview of Python
  • Different Applications where Python is Used
  • The Companies using Python
  • Values, Types, Variables
  • Operands and Expressions
  • Discuss Python Scripts on UNIX/Windows
  • Command Line Arguments
  • Conditional Statements
  • Loops
  • Writing to the Screen
  • Python files I/O Functions
  • Strings and related operations
  • Numbers
  • Lists and related operations
  • Tuples and related operations
  • Dictionaries and related operations
  • Sets and related operations
  • Functions
  • Variable Scope and Returning Values
  • Function Parameters
  • Global Variables
  • Standard Libraries
  • Modules Used in Python
  • Lambda Functions
  • Object Oriented Concepts
  • The Import Statements
  • Errors and Exception Handling
  • Module Search Path
  • Package Installation Ways
  • Handling Multiple Exceptions
  • Data Analysis
  • Operations on arrays
  • NumPy - arrays
  • Indexing, slicing and iterating
  • Pandas - data structures & index operations
  • Reading and writing arrays on files
  • Metadata for imported Datasets
  • Matplotlib library
  • Reading and Writing data from CSV/Excel formats into Pandas
  • Grids, axes, plots
  • Types of plots - bar graphs, pie charts, histograms
  • Markers, colours, fonts and styling
  • Contour plots
  • Basic Functionalities of a data object
  • Concatenation of data objects
  • Merging of Data objects
  • Exploring a Dataset
  • Types of Joins on data objects
  • Analyzing a dataset
  • Python Revision 
  • What is Machine Learning?
  • Machine Learning Process Flow
  • Machine Learning Use-Cases
  • Machine Learning Categories
  • Linear regression
  • What are Classification and its use cases?
  • Algorithm for Decision Tree Induction
  • Confusion Matrix
  • What is a Decision Tree?
  • Creating a Perfect Decision Tree
  • What is Random Forest?
  • Introduction to Dimensionality
  • PCA
  • Why Dimensionality Reduction
  • Scaling dimensional model
  • Factor Analysis
  • LDA
  • What is Naïve Bayes?
  • How Does Naïve Bayes Work?
  • What is a Support Vector Machine?
  • Implementing Naïve Bayes Classifier
  • Implementation of Support Vector Machine for Classification
  • Hyperparameter Optimization
  • Illustrate how Support Vector Machine works
  • Grid Search vs Random Search
  • What are Clustering & its Various Use Cases?
  • How K-means algorithm work?
  • What is K-means Clustering?
  • What is C-means Clustering?
  • How does Hierarchical Clustering work?
  • What is Hierarchical Clustering?
  • How to do optimal clustering?
  • What are Association Rules?
  • Calculating Association Rule Parameters
  • How do Recommendation Engines work?
  • Recommendation Engines
  • Association Rule Parameters
  • Content-Based Filtering
  • Collaborative Filtering
  • What is Reinforcement Learning?
  • Elements of Reinforcement Learning
  • Why Reinforcement Learning?
  • Epsilon Greedy Algorithm
  • Q values and V values
  • Exploration vs Exploitation dilemma
  • Q – Learning
  • Markov Decision Process (MDP)
  • Values
  • What is Time Series Analysis?
  • Components of TSA
  • Importance of TSA
  • AR model
  • White Noise
  • MA model
  • ARIMA model
  • ARMA model
  • Stationarity
  • ACF & PACF
  • What is Model Selection?
  • Cross – Validation
  • Need for Model Selection
  • How do Boosting Algorithms work?
  • What is Boosting?
  • Adaptive Boosting
  • Types of Boosting Algorithms
  • What is Exploratory Data Analysis?
  • EDA Classification
  • EDA Techniques
  • Univariate Graphical EDA
  • Multivariate Non-graphical EDA
  • Multivariate Graphical EDA
  • Heat Maps
  • Data Visualization
  • Business Intelligence tools
  • VizQL Technology
  • Connect to data from the File
  • Connect to data from the Database
  • Basic Charts
  • Chart Operations
  • Combining Data
  • Calculations
  • Trend lines
  • Reference lines
  • Geographic Maps
  • Forecasting
  • Clustering
  • Using charts effectively
  • Visual best practices
  • Dashboards
  • Story Points
  • Publish to Tableau Online

Data Science with Python Certification Course Projects

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Certificate For Data Science with Python Certification Course

The training will help clear the Data Science Python Certification Course Exam. The complete training course content is aligned with these certification programs and helps you quickly clear these certification exams and get the best jobs in the top companies. As part of the training, you will be working on real-time assignments and projects with practical implications in the real-world industry, helping you fast-track your career. Multiple quizzes at the end of this training program will perfectly reflect the questions in the actual certification exams and help you score better.

CERTIFICATE FOR Data Science with Python Certification Course
Your Name
Data Science with Python Certification Course
Issued By
Certificate ID __________
Date __________

Frequently Asked Questions on Data Science with Python Certification Course

What is the job outlook for Data Science With Python professionals?

The job role has marked an annual growth of 35% for Data scientists and engineers.

If you have prior coding experience, learning Python for Data Science will be easier. However, it is not compulsory.

  • Knowledge of programming languages like R, SQL, and Python
  • Knowledge of Statistics and other related concepts
  • Machine learning for effectively handling big sets of data
  • Data wrangling to refine data
  • Knowledge of Linear Algebra and Multivariable Calculus
  • Knowledge of data visualization tools for effortless communication of insights collected
  • Business Analyst
  • Big Data Engineer or Data Architect
  • Database Administrator
  • Business Intelligence (BI) Developer
  • Data Analyst
  • ML Engineer
  • Data Scientist
  • Business Intelligence Analyst
  • Statistician
  • Natural Language Processing Engineer
  • Computer Vision(CV) Engineer
  • MLOps Engineer
  • BI Managers 
  • Project Managers
  • Software Developers 
  • ETL Professionals
  • Analytics Professionals
  • Big Data Professionals

You don’t need prior knowledge for this Data Science with Python course. However, working knowledge of programming can help.