Online Data Science Course in Telugu

Are you ready to learn a Data Science course in Telugu? Entri offers online Data Science courses in your native language. It's time to delve into your favorite coding courses in Telugu. Secure your seats now!

100% Internship | Placement Assistance | Illinois Tech Certification
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Data science & Machine Learning Course తెలుగులో

80+ Recorded & Live Sessions

80+ Recorded & Live Sessions

Industry Expert Guidance

Industry Expert Guidance

Community Linking

Community Linking

Capstone Projects

Capstone Projects

Course Certificate

Course Certificate

Internship & Placement Support

Internship & Placement Support

Now Certified by Illinois Tech: Entri Elevate Data Science Course!

The Entri Elevate Data Science Course has received certification from Illinois Tech. Upon completion of the program, participants will receive prestigious badges from Illinois Tech. Designed for job seekers, this intensive training program focuses on analytics, machine learning, and data visualization, with a strong emphasis on placement.

Illinois Institute of Technology

Illinois Institute of Technology, commonly referred to as Illinois Tech, was established in 1890 as a private, research-oriented university with a strong emphasis on technology.

The institution provides a diverse range of undergraduate and graduate programs encompassing fields such as engineering, science, architecture, business, design, human sciences, applied technology, and law. With its unwavering commitment to technological advancements and academic excellence, Illinois Tech has earned a well-deserved reputation as one of the finest institutions for technology education worldwide.

Now Certified by Illinois Tech: Entri Elevate Data Science Course!

The Entri Elevate Data Science Course has received certification from Illinois Tech. Upon completion of the program, participants will receive prestigious badges from Illinois Tech. Designed for job seekers, this intensive training program focuses on analytics, machine learning, and data visualization, with a strong emphasis on placement.

Illinois Institute of Technology

Illinois Institute of Technology, commonly referred to as Illinois Tech, was established in 1890 as a private, research-oriented university with a strong emphasis on technology.

The institution provides a diverse range of undergraduate and graduate programs encompassing fields such as engineering, science, architecture, business, design, human sciences, applied technology, and law. With its unwavering commitment to technological advancements and academic excellence, Illinois Tech has earned a well-deserved reputation as one of the finest institutions for technology education worldwide.

Data Science Course - Our Professional Mentors

Learn Data Science in Telugu from Top-Notch Mentors

Learn Data Science and Machine Learning in Telugu without any language barriers

If language is a barrier to learning Data Science and Machine Learning, Entri App offers the course in Telugu. Learn your favorite course in Telugu without any hindrance.

  • Complete Data Science Course in Telugu
  • Learning in Telugu helps you learn easily
  • Ask & Clear Your Doubts in Your Native Language
  • Even the complex programming
  • No more language barriers
  • Easy to communicate & discuss with peers

Data Science Course in Telugu - Frequently Asked Questions

Here are some frequently asked questions on Data Science and Machine Learning that we have answered.

Yes! This course is specially designed for candidates from Andra Pradesh and Telangana region. So, the primary medium of communication will be in Malayalam.

After you complete the data science and machine learning course in Telugu, some of the career paths you can follow are Data Scientist, Data Engineer, Data Analyst, Machine learning Engineer, Marketing Analyst, Clinical Data Manager, Business IT Analyst. Apart from these, there are other options too, like Data Visualization Specialist, Artificial Intelligence Engineer, Data Product Manager, Data Marketing Analyst etc. In conclusion, the career opportunities in data science are vast and varied, and it largely depends on your interests, skills, and the type of industry you want to work in. Click the link below & check the wide range of career options available in Kerala- Data Science Jobs in Kerala/

The average salary for a Data Scientist is ₹14,00,000 per year in India. The average additional cash compensation for a Data Scientist in India is ₹2,00,000, with a range from ₹1,00,000 - ₹3,47,500.

Data science plays a critical role in helping companies make informed decisions based on data, and it is being applied in many industries, including healthcare, finance, retail, and technology. With the rise of big data and the increasing importance of data-driven decision-making, data science is poised for even greater growth and impact in the coming years. According to the US Bureau of Labor Statistics, the demand for Data Science jobs is expected to grow by 27.9 per cent by 2026 and 36% growth by 2031.

Of course! If you are planning to migrate from India to other countries, upskilling yourself in data science will be a true blessing. In Europe, countries such as the United Kingdom, Germany, and France have strong economies and a growing demand for data scientists. So, you will be able to get into a high-paying career anywhere in the world.

The Data Science course in Entri App is an online programme designed to provide a comprehensive understanding of Data Science and related technologies, such as Python, statistics, data analysis, machine learning, and Tableau. The course includes live projects and capstone projects to give you real-world experience in the field.

  • Preparatory Session for Programming
  • Python Programming
  • Data Analysis & Visualization
  • MySQL
  • Machine Learning with Python
  • Deep learning Fundamentals
  • Capstone Projects

Yes, Entri App provides career support through

  • Job opportunities portal to explore job options
  • Personalised industry mentorship for career guidance
  • Resume Review
  • Live profile-building workshops

Of Course! Our data science course is designed for beginners as well. The session covers all the fundamentals to put you on track.

Data science is a field that requires both technical skills and communication abilities. It involves programming proficiency in languages like Python and R, expertise in statistical analysis to identify patterns, and mastery of machine learning to develop intelligent systems. However, it is equally important to have soft skills such as data visualization and clear communication to translate raw data into meaningful insights that can drive business decisions. Imagine a technical expert with the investigative skills of a detective, who can weave compelling stories from the vast array of information available.

If you are interested in learning data science but don't have enough time to attend classes, then the entri App is the perfect solution for you.

You can learn the data science course online in your own native language. Pre-recorded videos, study materials, and everything else you need is provided to help you achieve your dream career.

Getting started with data science as a beginner involves a systematic approach to learning key skills and gaining practical experience. Here are some recommended steps:

  • Learn the Basics of Programming
  • Understand Data Manipulation and Analysis
  • Explore Data Visualization
  • Gain Statistical Knowledge
  • Learn Machine Learning Basics
  • Practice with Real-world Projects
  • Build a Portfolio

Remember that consistency and practical application are key in learning data science. Regularly practice coding, work on projects, and stay updated on industry trends to build a strong foundation in this dynamic field.

Data Science and machine Learning Course curriculum

Introduction to Programming

What is programming?, Compiler,Interpreter,
Source Code, Machine code, Algorithms,
Editors.

Introduction to Data Science

What is Data Science?, Job
Roles,Terminologies, data science
applications and its work flows.

Introduction to Excel

Introduction to Excel interface, Basics of
Excel, Soreadsheet Basics, Data Entry, Excel
Fundamentals, Insertion and deletion,
Importing data, Table creation.

Working with Formulas and Functions

SUM function, Min, Max function, COUNT
function, AVERAGE function, Functions
exercise, IF function, SUMIF, COUNTIF,
AVERAGEIF functions, Formatting text using
RIGHT, LEFT, MID functions, Formatting text
using Upper, LOWER and PROPER functions.

Data Cleaning in Excel

Introduction to Data Cleaning, Basics of Data Privacy, Removing duplicate data, Handling
missing data, Correcting inconsistent data, Splitting and merging data, Data

transformation, Removing unnecessary characters and spaces, Conditional formatting for
data cleaning.

Analyzing Data using Spreadsheets

Data exploration and summary statistics, Filtering and Sorting data in excel, VLOOKUP and
HLOOKUP for Excel, Power Query and data transformation, Introduction to Pivot tables,
Creating Pivot tables, Forecasting and trend analysis.

Capstone Project

Personal Finance, Removing duplicate data, Handling missing data, Correcting
inconsistent data, Dataset Creation, Data exploration and summary statistics, Filtering and
Sorting data in excel, Creating Pivot tables, Forecasting and trend analysis, Pivot Tables.

Module End Assignment 

Project Title - Sales Data Analysis
Analyze Sales data for a frictional retail
store and generate insights that can
inform business decisions.

Introduction to SQL and Installation

Database and DBMS, Installation of MySQL
Workbench, Overview of MySQL and MySQL
Workbench.

SQL Database

Database creation and removing it, Tables and its
concepts, SQL Data Types, Database creation and few
concepts.

Data Definition Language

Introduction to Data Definition Language, Creating
tables using CREATE TABLE, Specifying column names,
data types and constraints, ALTER TABLE statement,
Adding, modifying, and deleting columns, DROP TABLE
statement.

Data Manipulation Language 

Introduction to DML and its significance in SQL, SELECT
statement and its syntax, Filtering data using WHERE
clause, ORDER BY and LIMIT, SELECT, WHERE, ORDER BY
and LIMIT, INSERT statement, UPDATE statement,
DELETE statement, Using transactions to ensure data
consistency.

MySQL Functions And Joins

Built-in functions, User-defined functions,
JOINS in SQL, Working with multiple tables in
queries, Aggregating data with GROUP BY
and HAVING clauses, Using functions like
COUNT, SUM, AVG, MIN, and MAX, Difference
between WHERE and HAVING Clause.

MySQL Subqueries

Introduction to Subqueries, Scalar
Subqueries, Using comparison operators
(=, <>, <, >, etc.) with scalar subqueries,
Handling scenarios with subquery results
containing NULL values, Subqueries in
WHERE and HAVING Clauses, Subqueries in
FROM and JOIN Clauses, Best practices for
using subqueries effectively in SQL.

MySQL Stored procedures and Triggers

Stored procedures, Triggers, DCL
Commands, TCL Commands.

Module End Assignment

Project Title - Sales Data Analysis with SQL

Analyze Sales data for a frictional retail store
and generate insights that can inform
business decisions using SQL.

Power Bl Introduction

Understanding the Power BI Ecosystem, Installation of PowerBl Desktop, Exploring the Power BI
interface, Connecting to data sources.

Data modeling

Relationships in PowerBI, Cardinality, Building a star schema data model.

Data Cleaning using Power Query in Power BI

Introduction to Power Query and Data Cleaning, Importing and Loading Data into Power Query,
Working with Columns, Handling missing data, Duplicate Data, Filtering rows, Combining and merging
data, Aggregating and grouping data, Handling Data Quality Issues.

Data Analytics with Power Bl

What is DAX?, Calculated Columns using DAX, Measures, Calculated Tables in powerBl, DAX Concepts.

Data Visualization in Power BI

Bar Charts, Line and combo Charts, Scatter plot charts, Bubble plot charts, Explore different charts and
its usages, For the given datasets draw different charts and infer the insights, Map Visualization, Other
Useful Visualizations, Designing and formatting visuals.

Advanced Visualizations and Analytics

Working with matrices, tables, and cards, Creating hierarchies and drill-down visuals, Applying
conditional formatting and data bars.

Building Interactive Reports

Filters in PowerBl reports, Basic and Advanced filters, Cross filter and cross highlighting Slicers, Create
bookmarks and buttons, and Create a drillthrough button.

Creating Dashboard

Dashboards, Difference between dashboards and reports, Design a great PowerBI dashboard.

Data Visualization Best Practices 

Design principles for effective data visualizations, Applying color schemes and themes, Utilizing
custom visuals and marketplace extensions, Optimizing performance and responsiveness of reports.

Capstone Project

Personal Finance Project Introduction, Personal Finance Dashboard, Financial Performance Analysis
and importance.

Module End Assignment

Project Title - Financial Performance Analysis

Financial Performance analysis: to optimize financial reporting in a firm that provides customers to
track their financial health and productivity. Create data models and visualizations, and dashboards
and present the project results and insights. Or Users can choose a different domain based on their
interest and can choose datasets from kaggle or any other websites.

Introduction to Python

Introduction to Programming Languages and Python, Installing Anaconda and Jupyter Notebook,
Python variables and data types, Conditional statements.

Python functions and loops

Loops in Python, For Loop, While loop, Introduction to functions in Python, Defining functions in
Python, Calling functions in python, Function return statement.

Data Structures and Collections

Lists, tuples, and dictionaries, Accessing and manipulating elements in collections, Common
operations and methods on collections.

File Handling and Modules

Reading from and writing to files in Python, Working with text and CSV files, Introduction to modules
and libraries, Importing and using built-in and external modules.

NumPy and Pandas

Introduction to NumPy, NumPy arrays, Array operations and functions, Introduction to Pandas, Series,
Data Frame, Data ingestion.

Data Cleaning and Preprocessing

Introduction to Data Cleaning and Preprocessing, Data preprocessing steps, Data exploration and
data quality assessment, List comprehensions for concise data manipulation, Data type conversions,
Data Cleaning in Python, Handling missing values with Pandas, Data duplication and duplicate
handling with Pandas, Data normalization and scaling with Pandas, Best practices for data cleaning
with NumPy and Pandas.

Data Visualization in Python

Introduction to Data visualization in Python, Line chart, Bar chart, Pie chart.

Module End Assignment

Project Title - Store sales and profit analysis in Python.
Project should cover data cleaning and data visualization using pandas as matplotlib respectively.

Introduction to Machine Learning
Introduction to machine learning, Types of machine learning (supervised, unsupervised),
Applications of machine learning, Machine learning workflow.

Supervised Learning Algorithms

Supervised learning overview, Regression, Classification, Linear regression, Logistic regression,
Decision trees.

Evaluation and Model Selection
Model evaluation, Model evaluation metrics, Selecting best performing model, Cross-Validation.

Unsupervised Learning Algorithms

Unsupervised learning overview, Clustering, Dimensionality reduction, K-means clustering,
Hierarchical clustering, Principal Component Analysis (PCA), t-SNE (t-Distributed Stochastic
Neighbor Embedding).

Introduction to Neural Networks

Biological inspiration for neural networks, Perceptron's and activation functions, Feedforward neural networks.

Capstone Project

Importance of Machine Learning in Businesses, Career opportunities in the Data Field, Future Scope.

Module End Assignment

Project Title - Predict house prices

based on various features In this project, you will build a machine
learning model to predict house prices based on various features. The project will involve data preprocessing, feature engineering, model training, and evaluation.

Introduction to Programming

What is programming?, Compiler,Interpreter,
Source Code, Machine code, Algorithms,
Editors.

Introduction to Data Science

What is Data Science?, Job
Roles,Terminologies, data science
applications and its work flows.

Introduction to Excel

Introduction to Excel interface, Basics of
Excel, Soreadsheet Basics, Data Entry, Excel
Fundamentals, Insertion and deletion,
Importing data, Table creation.

Working with Formulas and Functions

SUM function, Min, Max function, COUNT
function, AVERAGE function, Functions
exercise, IF function, SUMIF, COUNTIF,
AVERAGEIF functions, Formatting text using
RIGHT, LEFT, MID functions, Formatting text
using Upper, LOWER and PROPER functions.

Data Cleaning in Excel

Introduction to Data Cleaning, Basics of Data Privacy, Removing duplicate data, Handling
missing data, Correcting inconsistent data, Splitting and merging data, Data

transformation, Removing unnecessary characters and spaces, Conditional formatting for
data cleaning.

Analyzing Data using Spreadsheets

Data exploration and summary statistics, Filtering and Sorting data in excel, VLOOKUP and
HLOOKUP for Excel, Power Query and data transformation, Introduction to Pivot tables,
Creating Pivot tables, Forecasting and trend analysis.

Capstone Project

Personal Finance, Removing duplicate data, Handling missing data, Correcting
inconsistent data, Dataset Creation, Data exploration and summary statistics, Filtering and
Sorting data in excel, Creating Pivot tables, Forecasting and trend analysis, Pivot Tables.

Module End Assignment 

Project Title - Sales Data Analysis
Analyze Sales data for a frictional retail
store and generate insights that can
inform business decisions.

Introduction to SQL and Installation

Database and DBMS, Installation of MySQL
Workbench, Overview of MySQL and MySQL
Workbench.

SQL Database

Database creation and removing it, Tables and its
concepts, SQL Data Types, Database creation and few
concepts.

Data Definition Language

Introduction to Data Definition Language, Creating
tables using CREATE TABLE, Specifying column names,
data types and constraints, ALTER TABLE statement,
Adding, modifying, and deleting columns, DROP TABLE
statement.

Data Manipulation Language 

Introduction to DML and its significance in SQL, SELECT
statement and its syntax, Filtering data using WHERE
clause, ORDER BY and LIMIT, SELECT, WHERE, ORDER BY
and LIMIT, INSERT statement, UPDATE statement,
DELETE statement, Using transactions to ensure data
consistency.

MySQL Functions And Joins

Built-in functions, User-defined functions,
JOINS in SQL, Working with multiple tables in
queries, Aggregating data with GROUP BY
and HAVING clauses, Using functions like
COUNT, SUM, AVG, MIN, and MAX, Difference
between WHERE and HAVING Clause.

MySQL Subqueries

Introduction to Subqueries, Scalar
Subqueries, Using comparison operators
(=, <>, <, >, etc.) with scalar subqueries,
Handling scenarios with subquery results
containing NULL values, Subqueries in
WHERE and HAVING Clauses, Subqueries in
FROM and JOIN Clauses, Best practices for
using subqueries effectively in SQL.

MySQL Stored procedures and Triggers

Stored procedures, Triggers, DCL
Commands, TCL Commands.

Module End Assignment

Project Title - Sales Data Analysis with SQL

Analyze Sales data for a frictional retail store
and generate insights that can inform
business decisions using SQL.

Power Bl Introduction

Understanding the Power BI Ecosystem, Installation of PowerBl Desktop, Exploring the Power BI
interface, Connecting to data sources.

Data modeling

Relationships in PowerBI, Cardinality, Building a star schema data model.

Data Cleaning using Power Query in Power BI

Introduction to Power Query and Data Cleaning, Importing and Loading Data into Power Query,
Working with Columns, Handling missing data, Duplicate Data, Filtering rows, Combining and merging
data, Aggregating and grouping data, Handling Data Quality Issues.

Data Analytics with Power Bl

What is DAX?, Calculated Columns using DAX, Measures, Calculated Tables in powerBl, DAX Concepts.

Data Visualization in Power BI

Bar Charts, Line and combo Charts, Scatter plot charts, Bubble plot charts, Explore different charts and
its usages, For the given datasets draw different charts and infer the insights, Map Visualization, Other
Useful Visualizations, Designing and formatting visuals.

Advanced Visualizations and Analytics

Working with matrices, tables, and cards, Creating hierarchies and drill-down visuals, Applying
conditional formatting and data bars.

Building Interactive Reports

Filters in PowerBl reports, Basic and Advanced filters, Cross filter and cross highlighting Slicers, Create
bookmarks and buttons, and Create a drillthrough button.

Creating Dashboard

Dashboards, Difference between dashboards and reports, Design a great PowerBI dashboard.

Data Visualization Best Practices 

Design principles for effective data visualizations, Applying color schemes and themes, Utilizing
custom visuals and marketplace extensions, Optimizing performance and responsiveness of reports.

Capstone Project

Personal Finance Project Introduction, Personal Finance Dashboard, Financial Performance Analysis
and importance.

Module End Assignment

Project Title - Financial Performance Analysis

Financial Performance analysis: to optimize financial reporting in a firm that provides customers to
track their financial health and productivity. Create data models and visualizations, and dashboards
and present the project results and insights. Or Users can choose a different domain based on their
interest and can choose datasets from kaggle or any other websites.

Introduction to Python

Introduction to Programming Languages and Python, Installing Anaconda and Jupyter Notebook,
Python variables and data types, Conditional statements.

Python functions and loops

Loops in Python, For Loop, While loop, Introduction to functions in Python, Defining functions in
Python, Calling functions in python, Function return statement.

Data Structures and Collections

Lists, tuples, and dictionaries, Accessing and manipulating elements in collections, Common
operations and methods on collections.

File Handling and Modules

Reading from and writing to files in Python, Working with text and CSV files, Introduction to modules
and libraries, Importing and using built-in and external modules.

NumPy and Pandas

Introduction to NumPy, NumPy arrays, Array operations and functions, Introduction to Pandas, Series,
Data Frame, Data ingestion.

Data Cleaning and Preprocessing

Introduction to Data Cleaning and Preprocessing, Data preprocessing steps, Data exploration and
data quality assessment, List comprehensions for concise data manipulation, Data type conversions,
Data Cleaning in Python, Handling missing values with Pandas, Data duplication and duplicate
handling with Pandas, Data normalization and scaling with Pandas, Best practices for data cleaning
with NumPy and Pandas.

Data Visualization in Python

Introduction to Data visualization in Python, Line chart, Bar chart, Pie chart.

Module End Assignment

Project Title - Store sales and profit analysis in Python.
Project should cover data cleaning and data visualization using pandas as matplotlib respectively.

Introduction to Machine Learning
Introduction to machine learning, Types of machine learning (supervised, unsupervised),
Applications of machine learning, Machine learning workflow.

Supervised Learning Algorithms

Supervised learning overview, Regression, Classification, Linear regression, Logistic regression,
Decision trees.

Evaluation and Model Selection
Model evaluation, Model evaluation metrics, Selecting best performing model, Cross-Validation.

Unsupervised Learning Algorithms

Unsupervised learning overview, Clustering, Dimensionality reduction, K-means clustering,
Hierarchical clustering, Principal Component Analysis (PCA), t-SNE (t-Distributed Stochastic
Neighbor Embedding).

Introduction to Neural Networks

Biological inspiration for neural networks, Perceptron's and activation functions, Feedforward neural networks.

Capstone Project

Importance of Machine Learning in Businesses, Career opportunities in the Data Field, Future Scope.

Module End Assignment

Project Title - Predict house prices

based on various features In this project, you will build a machine
learning model to predict house prices based on various features. The project will involve data preprocessing, feature engineering, model training, and evaluation.

Data Science and Machine Learning Course full Syllabus

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