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Master Diploma in Data Science with Artificial Intelligence

#1 Institute in IT Training Β· 25+ Years of Experience

Master Diploma in Data Science with Artificial Intelligence skills and take your career to the next level with comprehensive training in Advanced MS Excel, Python, MySQL, Power BI, R, Statistics, Machine Learning, NLP, TensorFlow and Neural Networks.

205+ Hours12 Months Duration Offline ClassroomMode of Training Data Science & AIDiploma Program AI & AnalyticsPython Β· R Β· Power BI Β· TensorFlow
β˜…β˜…β˜…β˜…β˜…4.9/5Google Reviewsβ€’1 Lakh+ learners
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Build Data Science & Artificial Intelligence Skills

The Diploma in Data Science with Artificial Intelligence is a 12-month, 205+ hour offline program covering analytics, programming, databases, business intelligence, statistics, machine learning, natural language processing and deep learning.

The supplied brochure covers Advanced MS Excel, Python, MySQL, Power BI, R, Statistics, Machine Learning, NLP, Artificial Neural Networks, TensorFlow, face detection and object detection.

Key Highlights

Advanced MS Excel

Advanced formulas, conditional formatting, lookup functions, Pivot Tables, charts, validation, What-If Analysis and data operations.

Python Programming

Learn Python fundamentals, data types, control statements, collections, OOPs, MySQL connectivity, NumPy and Pandas.

MySQL & Databases

Cover DBMS/RDBMS concepts, databases, tables, queries, keys, joins, clauses, indexes, users and privileges.

Power BI & DAX

Build reports and dashboards, work with Power Query and M, data models, relationships, hierarchies and DAX expressions.

R & Statistics

Learn R, R Studio, data manipulation, visualization, probability, distributions, hypothesis testing, Z-test, T-test and ANOVA.

Machine Learning & AI

Learn machine learning fundamentals, data preparation, train-test split and model evaluation.

Course Overview

This comprehensive diploma follows the supplied brochure from spreadsheet analytics and Python programming through MySQL, Power BI, R, statistics, machine learning, NLP and neural-network based AI topics.

205+ HoursTraining Duration
12 MonthsProgram Duration
OfflineMode of Training
Data Science + AIProgram Focus
ExcelPythonMySQL Power BIRR Studio NLTKTensorFlow

Program Curriculum

The curriculum below follows the supplied Diploma in Data Science with Artificial Intelligence brochure, retaining its subject areas and terminology.

Module 1Advanced MS Excel β€” MS Excel Introduction & FundamentalsβŒ„
  • 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.
  • 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
Module 2Advanced MS Excel β€” Functions, Filtering & AnalysisβŒ„
  • Today, Now, Day, Month, Year, Date, Datedif, Edate, EOMonth.
  • Time, Text, hour, minute and second.
  • Weekday, workday, workday.INTL, networkDay, Networkdays.INTL.
  • Advance Filters
  • Sorting and Filtering
  • Filtering on Text, Numbers & Colors
  • Number, Date & Time Validation
  • Text and List Validation
  • Dynamic Dropdown List Creation using Data Validation
  • 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/Vlookup/Hlookup/Xlookup
  • Index, Offset and Match function
  • Row, Rows, Column, Columns
  • Sort, unique
  • 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
Module 3Advanced MS Excel β€” Import, Export, Text, Mathematical & PivotβŒ„
  • Edit Custom List
  • Consolidate data
  • Conversion of Excel files to PDF/CSV/Notepad
  • Removing Duplicates & Flash Fill
  • Comments, Freeze Panes & Shortcut Keys
  • 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
  • Creating Simple Pivot Tables
  • Basic and Advanced value Field Setting
  • Classic Pivot Table and Choosing Field
  • Filtering Pivot Tables and Charts
  • Using Slicer
  • Worksheet Protection
  • Workbook Protection
  • Column Protection
Module 4Python β€” Introduction, Data Types & Control StatementsβŒ„
  • Python Features
  • Python History
  • Python Applications
  • Python Install
  • Print function
  • Text type
  • Numeric type
  • Sequence type
  • Mapping type
  • Set type
  • Boolean type
  • Binary type
  • If-else statements
  • While loop Statements
  • For loop statements
  • Switch case statements
  • Break statements
  • Continue statements
Module 5Python β€” Collections, OOPs & MySQLβŒ„
  • namedtuple()
  • Lists
  • Arrays
  • Tuples
  • Sets
  • Dictonary
  • Classes / Objects
  • Inheritance
  • Polymorphism
  • Encapsulation
  • Abstraction
  • MySQL Environment Setup
  • Database Connection
  • Creating New Database
  • Creating Tables
  • Insert, Read & Update Operation
  • Performing Transactions
Module 6NumPy & PandasβŒ„
  • Environment Setup & Ndarray.
  • Array From Existing Data.
  • Arrays within the numerical range.
  • Series, Series.map(), Series.std() & Series.value_counts().
  • Broadcasting & Array Iteration.
  • Bitwise Operators, String & Mathematical Functions.
  • Statistical Functions, Sorting & Searching.
  • DataFrame, DataFrame.append() & DataFrame.apply().
  • DataFrame.count()
  • DataFrame.iterrows() & DataFrame.merge()
  • DataFrame.pivot_table() & DataFrame.sort()
  • DataFrame.query() & DataFrame.shift()
Module 7Matplotlib & MySQLβŒ„
  • Line graph
  • Bar graph
  • Pie Chart
  • Histogram
  • Scatter plot
  • DBMS & RDBMS Concepts
  • MySQL History & Features
  • MySQL Data Types & Connection
  • Create Database
  • Select Database
  • Drop Database
  • Show Database
  • CREATE, ALTER & Show Table
  • Rename, Describe & TRUNCATE Table
  • DROP, Temporary & Copy Table
  • Add/Delete, Show & Rename Column
Module 8MySQL Queries, Functions, Views & User ManagementβŒ„
  • MySQL Queries
  • INSERT Record
  • UPDATE Record
  • DELETE Record
  • SELECT Record
  • Primary, Unique, Foreign & Default key
  • MySQL JOIN/INNER JOIN
  • MYSQL LEFT JOIN & RIGHT JOIN
  • MYSQL CROSS JOIN & SELF JOIN
  • MYSQL NATURAL JOIN
  • MySQL WHERE
  • MySQL DISTINCT
  • MySQL FROM
  • MySQL ORDER BY
  • MySQL GROUP BY & HAVING
  • MySQL AND & OR
  • MySQL AND OR & LIKE
  • MySQL IN & NOT
  • MySQL IS NULL & IS NOT NULL
  • MySQL BETWEEN
  • Create, Show, Unique & Drop index
  • MYSQL Create User
  • MYSQL Drop User
  • MYSQL Show Users
  • Change User Password
  • MYSQL Grant Privilege & Revoke Privilege
  • MYSQL IF() & IFNULL()
  • MYSQL NULLIF() & CASE
  • MySQL count() & sum()
  • MySQL avg(), min() & max()
  • CONCAT()
  • UPPER()
  • LOWER()
  • LENGTH()
  • SUBSTRING()
  • REPLACE()
  • TRIM()
  • ROUND()
  • CEIL()
  • FLOOR()
  • ABS()
  • MOD()
  • POWER()
  • NOW()
  • CURDATE()
  • CURTIME()
  • YEAR()
  • MONTH()
  • DAY()
  • DATEDIFF()
  • DATE_FORMAT()
  • What is a View?
  • CREATE VIEW
  • ALTER VIEW
  • DROP VIEW
  • Updatable views
  • Advantages of views
Module 9Power BI β€” Introduction, Reports & VisualizationβŒ„
  • Fundamentals of Power BI
  • 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
  • Report Design with Database Tables L
  • Understanding Power BI Report Designer
  • Report Canvas, Report Pages: Creation, Renames
  • β€œGET DATAβ€œ 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
  • Power BI Design: Canvas, Visualizations and Fields
  • Import Data Options with Power BI Model, Advantages
  • Report visualizations and properties
  • 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.
Module 10Power BI β€” Hierarchies, Charts & Power QueryβŒ„
  • Hierarchies and Drilldown Options
  • Hierarchy 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.
  • 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
  • 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.
Module 11Power Query, DAX & Power BI ServiceβŒ„
  • 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.
  • 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.
  • 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).
Module 12Data Visualization using RβŒ„
  • 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.
  • R Functions and Loops.
  • Control Statements
  • Special utility functions.
  • Merging and Sorting data.
  • Concepts of Packages.
  • Reshaping data Operators Functions Loops.
  • Arrays, User Define Function, Cleaning Data with R.
  • 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.
Module 13Statistics & Business StatisticsβŒ„
  • 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.
  • Conditional probability.
  • Normal distribution.
  • Uniform distribution and Frequency distribution.
  • Frequency distribution and Concept of Hypothesis Testing.
Module 14Machine Learning ConceptsβŒ„
  • What is Machine Learning?
  • History and Fundamentals of Machine Learning.
  • How artificial intelligence relates to machine learning.
  • Data science vs Machine Learning.
  • Branches of Machine Learning.
  • Data preparation for modelling Train test split
  • Evaluation of the model.
Module 15Deep Learning, Computer Vision, NLP & TensorFlowβŒ„
  • Intro to CNN
  • Type of layers
  • Activation Layer
  • Pooling
  • Flattening
  • Face and eye detection
  • Viola jones algorithm
  • Hair-like feature
  • Integral image
  • Training Classifiers and Adaptive Boosting
  • Cascading
  • Fully Connected Layer
  • Face Detection
  • Object Detection
  • Object Detection Overview
  • Understanding Faster RCNN
  • Implementing Mask RCNN in Python
  • Softmax, argmax and cross entropy
  • Perceptron
  • Gradient Descent
  • Back Propogation
  • LSTM Networks and CASE STUDY
  • Merging Faces and Yawn Detector and counter
  • 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
  • Artificial Neural Network
  • Intro to ANN and Perceptron
  • MNIST Case study
  • TensorFlow And Neural Network
  • Introduction to tensor flow and Neural Networks
  • Installing TensorFlow (CPU/GPU)
  • TensorFlow Tools and Ecosystem

Tools & Technologies You'll Learn

The brochure cover displays Excel, Python, MySQL, NLTK, Power BI, R, R Studio and TensorFlow.

ExcelPythonMySQL Power BIRR Studio NLTKTensorFlow

Career-Ready Data Science & AI Skills

The program combines analytics, programming, business intelligence, statistical analysis, machine learning and AI-focused topics as listed in the brochure.

Advanced Excel Analytics

Work with advanced formulas, filtering, data validation, lookup functions, Pivot Tables, charts and What-If Analysis.

Python Development

Build programming foundations with Python, collections, OOPs and data-science modules including NumPy and Pandas.

Database & SQL Skills

Use MySQL for databases, tables, queries, joins, keys, clauses, indexes, users and privileges.

Power BI Reporting

Create reports and dashboards with Power BI, Power Query, M Language, data models, relationships and DAX.

Statistical Analysis

Learn probability, distributions, hypothesis testing, Z-test, T-test, Chi-Square, one-way and two-way ANOVA.

Machine Learning & AI

Understand machine learning fundamentals, data preparation, train-test split and model evaluation.

Learning & Practical Support

Structured Learning

The syllabus moves from Excel and Python foundations into databases, BI, statistics and advanced AI topics.

Offline Training

The brochure lists Offline as the mode of training for this diploma program.

Industry-Relevant Curriculum

The syllabus combines analytics, programming, BI, statistics, machine learning and AI technologies.

Data Analysis & Visualization

Practice with Excel, Pandas, Matplotlib, Power BI and R-based data visualization topics.

AI & Machine Learning

The brochure includes machine learning fundamentals, data preparation, train-test split and model evaluation.

Practical Technology Stack

The brochure cover displays Excel, Python, MySQL, NLTK, Power BI, R, R Studio and TensorFlow.

Why Choose NIPSTec

The brochure highlights 25+ years of experience in training students and corporates, PAN India reach, qualified and experienced trainers, job-oriented programs, industry-relevant curriculum, ISO 9001:2015 certification and trust across corporates, PSUs, government and private sector.

25+ Years of Experience

Experience in training students and corporates.

Qualified & Experienced Trainers

Experienced trainers are highlighted as a core NIPSTec strength.

Industry-Relevant Curriculum

The diploma covers modern data, analytics and AI technologies listed in the brochure.

Job-Oriented Programs

NIPSTec identifies its programs as job-oriented.

PAN India Reach

The brochure highlights PAN India reach.

ISO 9001:2015 Certified

The brochure identifies NIPSTec as an ISO 9001:2015 certified company.

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Diploma in Data Science with Artificial Intelligence FAQs

Common questions about the diploma program

What is the duration of the diploma?οΌ‹

The supplied brochure lists 12 Months and 205+ Hours Duration.

Is the training offline?οΌ‹

Yes. The brochure lists Offline as the mode of training.

Which technologies are covered?οΌ‹

The brochure covers Advanced MS Excel, Python, MySQL, Power BI, R, R Studio, NLTK and TensorFlow.

Does the course include Python?οΌ‹

Yes. Python features, history, applications, installation, data types, control statements, collections, OOPs, MySQL, NumPy and Pandas are included.

Does the course cover Power BI and DAX?οΌ‹

Yes. The brochure includes Power BI reporting, visualization, Power Query and M Language, DAX expressions, Power BI Service and data modeling.

Does the program include R and Statistics?οΌ‹

Yes. R, R Studio, data manipulation, visualization, probability, distributions, hypothesis testing, Z-test, T-test, Chi-Square and ANOVA are listed.

Where is the NIPSTec training location shown on the brochure?οΌ‹

The brochure's contact information lists D-82, First Floor, Malviya Nagar, New Delhi - 110017 (India).

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