What is the salary of data science and AI?

The salary for professionals in Data Science and AI depends on experience, skills, and location. Entry-level positions in India typically offer between INR 6 to 10 lakhs per annum, while mid-level professionals earn between INR 12 to 20 lakhs. For senior roles or AI specialists, salaries can exceed INR 25 lakhs per annum, with opportunities for higher earnings globally.

Is data science and AI a good career?

Yes, Data Science and AI are among the most sought-after careers today. The demand for professionals with skills in data analysis, machine learning, and AI is growing rapidly across industries such as healthcare, finance, retail, and technology. With the right skill set, individuals in this field can expect strong job prospects, career growth, and attractive salaries.

Data Science and Artificial Intelligence

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Data Science and Artificial Intelligence

Elevate Your Career with NIPSTec’s Data Science and Artificial Intelligence Course in Malviya Nagar, Delhi

Step into the future of technology with NIPSTec’s Data Science and Artificial Intelligence Course in Malviya Nagar, Delhi. This advanced program is designed for professionals and students who want to master the art and science of Data Analysis and AI, equipping you with the skills needed to excel in these rapidly growing fields.

Our course provides a thorough exploration of key topics, including data analysis, statistical modeling, machine learning, AI algorithms, and data visualization. By combining theory with practical applications, you’ll learn how to interpret complex data, build predictive models, and implement AI solutions that drive innovation and efficiency.
Located in Malviya Nagar, Delhi, NIPSTec offers an interactive learning environment with experienced instructors who provide personalized guidance. Whether you are looking to break into the industry or enhance your existing skills, our course will help you gain the expertise to stand out in the competitive job market.

Key Highlights:

Comprehensive Curriculum: Our program covers all critical aspects of data science and AI, from basic data manipulation to advanced machine learning techniques, making it one of the best data science courses available.
Hands-On Learning: Engage in practical projects and real-world case studies that allow you to apply your knowledge and build a strong portfolio of work.
Career Advancement: Completing our Data Science and Artificial Intelligence course will enhance your resume and open up exciting career opportunities in data science, AI development, and beyond.
Expert Instructors: Learn from seasoned professionals who offer expert insights and personalized support, helping you to master both Data Science and AI technologies.

Enroll in NIPSTec’s Data Science and Artificial Intelligence Course in Malviya Nagar, Delhi today and take the next step towards becoming an expert in these transformative fields.

Yes, I am interested in my Career Growth!

NIPSTec Advantages

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Latest Curriculum

Classroom Training

Excellent Feedback

Free Counselling &
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Easy to Learn
Pedagogy

State-of-the-Art
Infrastructure

After Training
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Well Equipped
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Experienced &
Qualified Trainer

Key Learnings - Data Science And Artificial Intelligence

Advanced MS Excel

MS Excel Introduction

MS Excel Introduction.

Introduction to excel worksheet, Row, Columns, Cells etc.

Insert and delete worksheet, row and column.

Rename the sheet and delete multiple worksheets.

Customizing the Ribbon.

Formatting and Proofing

Currency format, Formatting Dates, Custom and special formats & Customizing Header & Footers

Formatting cells with number formats, Font formats, Alignment, Borders, etc

Basic and advance conditional formatting

Printer Properties and Page Setup for Printing.

Insert the Logo to your worksheet while printing.

Various Chart i.e. Bar Charts/Column Charts/ Pie Charts/ Line Charts

Date and Time Functions

Today, Now, Day, Month, Year, Date, Datedif, Edate, EOMonth.

Time, Text, hour, minute and second.

Weekday, workday, workday.INTL, networkDay, Networkdays.INTL.

Sorting and Filtering

advance Filters.

Sorting and Filtering.

Filtering on Text, Numbers & Colors.

Data Validation

Number, Date & Time validation.

Text and List Validation.

Dynamic Dropdown List Creation using Data Validation.

Name Manager & What If Analysis

Scenario Analysis & Data Tables.

Creating, Editing, and Deleting of Names.

Discussion on Name Ranges and Apply the Name Ranges on Cell and the combination of Cells.

Lookup & References Functions

Lookup/Vlookup/Hlookup/Xlookup.

Index, Offset and Match function.

Row, Rows, Column, Columns.

Sort, unique.

Statical & Other Functions

Average, Averaga, Sum, Count, Counta, Max, Maxa, Min, Mina.

Countblank, Large, Small, Median, Mode, Stdev And Var

Dsum, Dmax, Dmin, Daverage, Dcounta.

Pmt, Switch, Valuetotext, Yearfrac, Sequence, Sort And Filter.

Import & Export and Other

Edit Custom List.

Consolidate data.

Conversion of Excel files to PDF/CSV/Notepad.

Removing Duplicates & Flash Fill.

Comments, Freeze Panes & Shortcut Keys.

Text and Mathematic function

Concatenate, Concate, Upper, Lower and Proper.

Len, Trim, Left, Right, Mid, Find and Replace.

Search, Substitute, Exact and Rept.

Sumif, Sumifs, Countif, Countifs and Averageif.

Averageifs, if, ifs, Abs, Sign and power.

not, Ifs, Iferror and Rank.

Round, Roundup, Rounddown and Mround.

Pivot Tables & Charts

Creating Simple Pivot Tables.

Basic and Advanced value Field Setting.

Classic Pivot Table and Choosing Field.

Filtering Pivot Tables and Charts.

Using Slicer.

Excel Security

Worksheet Protection.

Workbook Protection.

Column Protection.

Python

Introduction To Python

Python Features.

Python History.

Python Applications.

Python Install.

Print function.

Data Types

Text type.

Numeric type.

Sequence type.

Mapping type.

Set type.

Boolean type.

Binary type.

Control Statements

If-else statements.

While loop Statements.

For loop statements.

Switch case statements.

Break statements.

Continue statements.

Collection Module

namedtuple().

Lists.

Arrays.

Tuples.

Sets.

Dictonary.

OOPs

Classes / Objects.

Inheritance.

Polymorphism.

Encapsulation.

Abstraction.

Python MySQL

MySQL Environment Setup.

Database Connection.

Creating New Database.

Creating Tables.

Insert, Read & Update Operation.

Performing Transactions.

My SQL

MySQL Overview

DBMS & RDBMS Concepts

MySQL History & Features

MySQL Data Types & Connection

MySQL Database

Create Database

Select Database

Drop Database

Show Database

Table & Views

CREATE, ALTER & Show Table

Rename, Describe & TRUNCATE Table

DROP, Temporary & Copy Table

Add/Delete, Show & Rename Column

MySQL Queries

INSERT Record

UPDATE Record

DELETE Record

SELECT Record

MySQL Clauses

MySQL WHERE

MySQL DISTINCT

MySQL FROM

MySQL ORDER BY

MySQL GROUP BY & HAVING

MySQL Conditions

MySQL AND & OR

MySQL AND OR & LIKE

MySQL IN & NOT

MySQL IS NULL & IS NOT NULL

MySQL BETWEEN

MySQL Key & Join

Primary, Unique, Foreign & Default key

MySQL JOIN/INNER JOIN

MYSQL LEFT JOIN & RIGHT JOIN

MYSQL CROSS JOIN & SELF JOIN

MYSQL NATURAL JOIN

MySQL Indexes & User Management

Create, Show, Unique & Drop index

MYSQL Create User

MYSQL Drop User

MYSQL Show Users

Change User Password

MySQL Privileges, Control Flow Function

MYSQL Grant Privilege & Revoke Privilege

MYSQL IF() & IFNULL()

MYSQL NULLIF() & CASE

MySQL count() & sum()

MySQL avg(), min() & max()

Power BI

Introduction to Power BI

Introduction to Power BI - Need, Imprtance.

Power BI - Advantages and Scalable Options.

History - Power View, Power Query, Power Pivot .

Business Analyst Tools, MS Cloud Tools.

Power BI Installation and Cloud Account.

Power BI Cloud, service, architecture and Data Access.

Sample Reports and Visualization Controls.

Creating Power BI reports, Auto filters

Report Design with Database Tables L

Understanding Power BI Report Designer

Report Canvas, Report Pages: Creation, Renames

“GET DATA“ Options and Report Fields, Filters

Report Design using Databases & Queries

Building Home Page & Blog Section

Stacked bar chart, Stacked column chart, Clustered bar chart, Clustered column chart.

Report visualizations and properties

Power BI Design: Canvas, Visualizations and Fields.

Import Data Options with Power BI Model, Advantages.

Creating Customised Tables with Power BI Editor.

Alternate Text and Tiles. Header (Column, Row) Properties.

Table Styles & Alternate Row Colours - Static, Dynamic.

Sparse, Flashy Rows, Condensed Table Reports. Focus Mode.

Column Headers, Column Formatting, Value Properties.

Chart and map Report properties

Stacked bar chart, stacked column chart, clustered bar chart, clustered column chart.

Line charts, area charts, stacked area charts.

Line and stacked row charts, line and stacked column charts.

Waterfall chart, scatter chart, pie chart.

Field Properties: Axis, Legend, Value, Tooltip, Colour Saturation, Filters Types.

Data Labels: Visibility, Colour and Display Units, Precision, Position, Text Options.

Hierarchies and Drilldown reports

Hierarchies and Drilldown Options.

UHierarchy Levels and Drill Modes - Usage.

Drill-thru Options with Tree Map and Pie Chart.

Higher Levels and Next Level Navigation Options.

Multi Field Aggregations and Hierarchies in Power BI.

Toggle Options with Tabular Data. Filters.

Drilldown Buttons and Mouse Hover Options @ Visuals.

Power Query & M Language - Part 1

Understanding Power Query Editor - Options.

Power BI Interface and Query / Dataset Edits.

Working with Empty Tables and Load / Edits.

Data Imports and Query Marking in Query Editor.

Query Rename, Load Enable and Data Refresh Options.

REPLACE, REMOVE ROWS, REMOVE COL, BLANK - M Lang.

Column Splits and FilledUp / FilledDown Options.

Creating Query Groups and Query References. Usage.

Power Query & M Language - Part 2

Invoke Function and Freezing Columns.

Creating Reference Tables and Queries.

Detection and Removal of Query Datasets.

Blank Queries and Enumeration Value Generation.

Append data in different data source Merge data from multiple excel file/ or difference data source.

Blank Queries and Enumeration Value Generation.

DAX Expressions

DAX EXPRESSIONS - Level 1.

Scope of Usage with DAX. Usability Options.

DAX Context : Row Context and Filter Context.

Parenthesis, Comparison, Arthmetic, Text, Logic .

Filter, Aggregation and Time Intelligence Functions.

Syntax Requirements with DAX. Differences with Excel.

Power BI Service & Data Modeling

Creating reports and dashboard.

Publishing reports on Power BI Service.

Using Power BI Service for operations on reports.

Publishing reports to Power BI Service for sharing and collaboration.

Creating relationships between tables.

Building data models with calculated columns and measures using DAX (Data Analysis Expressions).

Data Visualization using R

Introduction Of R

Installation of R & R Studio.

Reading and Writing data files and History of R.

Features and Variable Operators in R.

Working with R data frames.

Loading Vectors and Combining to Vectors in R.

Sorting and Filtering, Renaming, Formatting.

Control Statements

R Functions and Loops.

Special utility functions.

Merging and Sorting data.

Concepts of Packages.

Reshaping data Operators Functions Loops.

Arrays, User Define Function, Cleaning Data with R.

Data Types

Data Structure & Data Types.

Importing Data from various sources (txt, dlm, excel, csv etc ).

Database Input Exporting data to various formats.

Viewing Data Variable & Value Labels.

Data Manipulation Steps.

Need for Data Visualization.

Statistics

Basics of Statistics and Method

Data types and its measures.

Probability Applications and distribution with examples.

Various graphic representation with data for analysis.

Continuous probability distribution.

Z-test, T-test and Chi-Square Test.

One Way Anova and Two Way Anova Test.

Business Statics and Applications

Business Statics and Applications.

Conditional probability.

Normal distribution.

Uniform distribution and Frequency distribution.

Frequency distribution and Concept of Hypothesis Testing.

Introduction to Machine Learning

What is Machine Learning?

History and Fundamentals of Machine Learning.

How artificial intelligence relates to machine learning.

Data science vs Machine Learning.

MACHINE LEARNING CONCEPTS.

Branches of Machine Learning.

Data preparation for modelling Train test split Evaluation of the model.

Frequently Asked Questions (FAQ)

What is AI and advanced data analytics?

What is the salary of AI data analyst?

Who is eligible for the advanced data analytics and AI course?

How can I pay for the course fee?

Is there a possibility of paying the fee in installments for this course?

Are the trainers at NIPSTec highly experienced and knowledgeable in their respective fields?

For any queries or assistance, please Contact us

Data Science and Artificial Intelligence

Form Submitted

Key Learnings - Data Science And Artificial Intelligence

Advanced MS Excel

Formatting and Proofing

Date and Time Functions

Sorting and Filtering

Data Validation

Name Manager & What If Analysis

Lookup & References Functions

Statical & Other Functions

Import & Export and Other

Text and Mathematic function

Pivot Tables & Charts

Excel Security

Python

Introduction To Python

Data Types

Control Statements

Collection Module

OOPs

Python MySQL

My SQL

MySQL Overview

MySQL Database

Table & Views

MySQL Queries

MySQL Clauses

MySQL Conditions

MySQL Key & Join

MySQL Indexes & User Management

MySQL Privileges, Control Flow Function

Power BI

Introduction to Power BI

Creating Power BI reports, Auto filters

Report visualizations and properties

Chart and map Report properties

Hierarchies and Drilldown reports

Power Query & M Language - Part 1

Power Query & M Language - Part 2

DAX Expressions

Power BI Service & Data Modeling

Data Visualization using R

Introduction Of R

Control Statements

Data Types

Statistics

Basics of Statistics and Method

Business Statics and Applications

Introduction to Machine Learning

Frequently Asked Questions (FAQ)