‹›

Diploma in Data Science and Artificial Intelligence

Q: Is a data science diploma worth it?

Yes, a diploma in data science is highly valuable, especially in today’s data-driven world. NIPSTEC’s Diploma in Data Science and AI equips you with essential skills in data analysis, machine learning, and AI, making you job-ready for the booming tech industry. With the rising demand for data scientists, this diploma offers excellent career prospects.

Q: What is the salary of a diploma holder in data science?

In India, the salary for a diploma holder in data science typically ranges from ₹5,00,000 to ₹10,00,000 per annum for entry-level positions. With experience, professionals can earn significantly more, especially if they specialize in areas like machine learning or artificial intelligence. NIPSTEC’s Diploma in Data Science and AI prepares you to excel in these high-paying roles.

Q: Who is eligible for a data science diploma?

Anyone with a basic understanding of mathematics and a passion for technology can enroll in NIPSTEC’s Diploma in Data Science and AI. This course is ideal for students, working professionals looking to upskill, or individuals seeking a career change. No prior coding experience is required, though a background in statistics or programming can be beneficial.

Accelerate Your Career with a Diploma in Data Science & AI in Malviya Nagar

Take a deep dive into the world of technology with our Diploma in Data Science & AI in Malviya Nagar, Delhi. NIPSTec offers a cutting-edge diploma program designed for aspiring data scientists and AI professionals eager to develop the skills necessary to thrive in today’s data-driven world.
Our comprehensive diploma program covers a wide range of topics, including Machine Learning, Deep Learning, Python Programming, Data Analysis, and AI algorithms. Taught by experienced industry professionals, this course combines theoretical foundations with practical, real-world applications to ensure you’re job-ready by the time you graduate.
Situated in Malviya Nagar, our offline data science classes provide easy access to students and professionals across Delhi. By choosing our in-person training, you’ll benefit from face-to-face mentorship, real-time problem-solving, and the opportunity to build strong connections with peers and instructors.
Begin your journey towards a rewarding career by enrolling in our Diploma in Data Science & AI today!

Key Highlights:

Comprehensive Curriculum: Our data science course is designed to provide you with a solid foundation in both data science and artificial intelligence. This diploma is ideal for both beginners and professionals looking to advance their knowledge in this rapidly growing field.
Practical Training: Experience practical learning through hands-on projects, case studies, and real-world datasets. You'll develop key skills in Python, Machine Learning, and AI, ensuring you gain the experience needed to tackle real-world challenges.
Certification: Upon successful completion of the program, you will receive a diploma in Data Science & AI. This certification will validate your expertise and give you a competitive edge in the job market.
Expert Instructors: Engage with fellow learners who share your passion for data science and AI. Our interactive classroom environment encourages collaboration, allowing you to form valuable connections within the data science community.

Join the best diploma in data science & AI in Delhi and start building a future-proof career. Enroll now at NIPSTec and turn your passion for data and technology into a thriving profession!

Yes, I am interested in my Career Growth!

NIPSTec Advantages

Live Projects

Online / Webinar

Audio-Visual Aids

Latest Curriculum

Classroom Training

Excellent Feedback

Free Counselling &
Placement Assistance

Easy to Learn
Pedagogy

State-of-the-Art
Infrastructure

After Training
Support

Well Equipped
Library

Experienced &
Qualified Trainer

Key Learnings - Diploma in 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.

Natural Language Processing

NLP, NLTK, Nltk extension and exploration.

Description of sentiment analyzer.

Preprocessing- Tokenization/Tokens to vectors.

Sentiment Analysis using Logistic Regression.

Sentiment Lexicons Regular Expressions and Twitter sentiment Analysis.

Latent Sentiment Analysis

Intro to LSA PCA and SUD LSA in Python.

Advanced LSA.

Introduction to article spinning.

N- gram model.

Implementing article spinning with Python.

TensorFlow And Neural Network

Introduction to tensor flow and Neural Networks.

Advanced LSA.

Introduction to article spinning.

Implementing article spinning with Python.

Introduction to tensor flow and Neural Networks.

Artificial Neural Network

Intro to ANN and Perceptron.

MNIST Case study.

Intro to CNN Type of layers Activation Layer Pooling Flattening.

Fully Connected Layer.

Softmax, argmax and cross entropy Perceptron.

Gradient Descent Back Propogation.

LSTM Networks and CASE STUDY.

Object Detection

Object Detection Overview.

Understanding Faster RCNN.

Implementing Mask RCNN in Python.

Face Detection

Face and eye detection.

Viola jones algorithm.

Hair-like feature Integral image.

Training Classifiers and Adaptive Boosting Cascading.

Merging Faces and Yawn Detector and counter.

Frequently Asked Questions (FAQ)

Is a data science diploma worth it?

What is the salary of a diploma holder in data science?

Who is eligible for a data science diploma?

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?

Our Student`s Speak

For any queries or assistance, please Contact us

Diploma in Data Science and Artificial Intelligence

Form Submitted

Key Learnings - Diploma in 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

Natural Language Processing

Latent Sentiment Analysis

TensorFlow And Neural Network

Artificial Neural Network

Object Detection

Face Detection

Frequently Asked Questions (FAQ)

Our Student`s Speak