1 Lakh+ Learners Trained
PAN India Training & Development
ISO 9001:2015 Certified
25+ Years of Training Experience
Home/ Courses/ Master Data Science with Artificial Intelligence

Master Data Science with Artificial Intelligence

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

Master 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 and Machine Learning.

160+ Hours8 Months Duration Offline ClassroomMode of Training Data Science & AIMaster Program Analytics StackExcel Β· Python Β· MySQL Β· Power BI Β· R
β˜…β˜…β˜…β˜…β˜…4.9/5Google Reviewsβ€’1 Lakh+ learners
⬇ Download Brochure

Master Data Science & Artificial Intelligence Skills

The Master Data Science with Artificial Intelligence program is an 8-month, 160+ hour offline training program covering Advanced MS Excel, Python, MySQL, Power BI, R, Statistics and Machine Learning.

The supplied brochure also highlights NIPSTec's 25+ years of training experience, PAN India reach, qualified and experienced trainers, job-oriented programs and industry-relevant curriculum.

Key Highlights

Advanced MS Excel

Learn Excel fundamentals, formatting, charts, date and time functions, filtering, validation, lookup functions, Pivot Tables and security.

Python Programming

Build Python foundations with features, data types, control statements, collections, OOPs and Python-MySQL connectivity.

MySQL & Database Skills

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

Power BI & DAX

Create Power BI reports and dashboards, use Power Query and M Language, hierarchies, drilldown, DAX and data modeling.

R & Data Visualization

Learn R and R Studio, data frames, vectors, sorting, functions, packages, reshaping, data cleaning and visualization.

Statistics & Machine Learning

Study probability, distributions, hypothesis testing, statistical tests and core machine learning concepts, modelling and evaluation.

Course Overview

This master program follows the supplied brochure from advanced spreadsheet analytics and Python through NumPy, Pandas, Matplotlib, MySQL, Power BI, R, Statistics and introductory Machine Learning concepts.

160+ HoursTraining Duration
8 MonthsProgram Duration
OfflineMode of Training
Data Science + AIProgram Focus
ExcelPythonNumPy PandasMatplotlibMySQL Power BIDAXR R StudioStatisticsMachine Learning

Program Curriculum

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

Module 1Advanced MS Excel β€” Fundamentals & FormattingβŒ„
  • 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 & What-If 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 FunctionsβŒ„
  • 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„
  • Introduction To Python
  • 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.
  • Broadcasting & Array Iteration.
  • Bitwise Operators, String & Mathematical Functions.
  • Statistical Functions, Sorting & Searching.
  • Series, Series.map(), Series.std() & Series.value_counts().
  • 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, Keys, Joins, Clauses & 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()
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, Drilldown, 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.
  • Control Statements
  • 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.
  • 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.
  • 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.
  • MACHINE LEARNING CONCEPTS.
  • Branches of Machine Learning.
  • Data preparation for modelling Train test split
  • Evaluation of the model.

Tools & Technologies You'll Learn

The brochure's Tools to Master section lists Excel, Python, MySQL, Power BI, R and R Studio.

ExcelPythonMySQL Power BIRR Studio

Career-Ready Data Science Skills

The program combines spreadsheet analytics, programming, databases, business intelligence, data visualization, statistics and machine learning topics as listed in the brochure.

Advanced Excel Analytics

Use advanced formulas, filters, validation, lookup functions, Pivot Tables, charts and What-If Analysis.

Python & Data Handling

Build programming skills with Python, collections, OOPs, NumPy and Pandas.

Database & SQL Skills

Work with MySQL databases, tables, queries, joins, keys, clauses, indexes and user privileges.

Power BI Reporting

Create reports, visualizations and dashboards using Power BI, Power Query, DAX and data modeling.

R & Statistics

Learn R/R Studio, data manipulation and visualization, probability, distributions and statistical testing.

Machine Learning

Understand machine learning fundamentals, its relation to AI, branches, data preparation, train-test split and model evaluation.

Learning & Practical Support

Structured Learning

The syllabus progresses from Excel and Python into databases, BI, R, statistics and machine learning.

Offline Training

The brochure lists Offline as the mode of training for the 8-month program.

Industry-Relevant Curriculum

The brochure identifies the curriculum as industry relevant and job-oriented.

Data Analysis & Visualization

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

Programming & Database Skills

Develop Python and MySQL skills alongside data analysis and reporting tools.

Practical Technology Stack

The brochure's Tools to Master section highlights Excel, Python, MySQL, Power BI, R and R Studio.

Why Choose NIPSTec

The brochure highlights 25+ years of experience in training students and corporates, PAN India reach, trust by corporates, PSUs, government and private sector, qualified and experienced trainers, job-oriented programs and industry-relevant curriculum. It also identifies NIPSTec as an ISO 9001:2015 certified company.

25+ Years of Experience

Experience in training students and corporates.

Qualified & Experienced Trainers

Qualified and experienced trainers are highlighted by the brochure.

Industry-Relevant Curriculum

The brochure highlights industry-relevant curriculum and job-oriented programs.

Job-Oriented Programs

NIPSTec identifies its programs as job-oriented.

PAN India Reach

The brochure highlights PAN India reach and trust across multiple sectors.

ISO 9001:2015 Certified

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

Enquire About Master Data Science with Artificial Intelligence

Submit your details to get course information, batch guidance and admission assistance.

Interested in Data Science with Artificial Intelligence (AI)?

Share your details β€” our counsellor will call you within 24 hours.

Master Data Science with Artificial Intelligence FAQs

Common questions about the master program

What is the duration of the program?οΌ‹

The supplied brochure lists 8 Months and 160+ 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 Excel, Python, MySQL, Power BI, R and R Studio, with Statistics and Machine Learning in the curriculum.

Does the course include Python and NumPy/Pandas?οΌ‹

Yes. Python fundamentals, data types, control statements, collections, OOPs, Python-MySQL, NumPy and Pandas are listed.

Does the course cover MySQL?οΌ‹

Yes. The brochure includes DBMS/RDBMS concepts, database and table operations, queries, keys, joins, clauses, indexes, user management and privileges.

Does the program include Power BI and DAX?οΌ‹

Yes. Power BI reports, visualization, hierarchies, drilldown, Power Query, M Language, DAX, Power BI Service and data modeling are included.

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.

What Machine Learning topics are listed?οΌ‹

The brochure lists what machine learning is, its history and fundamentals, its relationship with AI, data science vs machine learning, branches, data preparation, train-test split and model evaluation.

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).

⬇ Download Brochure