What is the salary of an AI data analyst?

The salary of an AI data analyst varies based on experience, location, and company. In India, entry-level AI data analysts can expect a salary ranging from INR 5 to 8 lakhs per annum, while experienced professionals may earn upwards of INR 12 to 20 lakhs per annum. Salaries can be even higher in specialized roles or multinational companies.

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Advanced Data Analytics and Artificial Intelligence

Q: What is AI and advanced data analytics?

AI (Artificial Intelligence) and advanced data analytics are interconnected fields. AI refers to the development of machines and systems that can perform tasks typically requiring human intelligence, while advanced data analytics uses sophisticated techniques to analyze large datasets. When combined, they enhance data analysis through automation, improved accuracy, and predictive analytics.

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

The Advanced Data Analytics and Artificial Intelligence course is suitable for graduates in fields such as computer science, engineering, mathematics, or related disciplines. Individuals with a basic understanding of programming, statistics, and data analysis will benefit most from the course. However, anyone interested in upskilling in AI and data analytics can join, provided they have a willingness to learn these advanced concepts.

Unlock the future with NIPSTec’s Advanced Data Analytics and Artificial Intelligence Course in Malviya Nagar, Delhi.

Transform your career with NIPSTec’s Advanced Data Analytics and Artificial Intelligence Course in Malviya Nagar, Delhi .

This cutting-edge program is designed for professionals and students eager to delve into the fields of data analytics and AI, equipping you with the skills needed to excel in today’s data-driven world.
Our comprehensive curriculum covers a range of topics, including advanced data analytics techniques, machine learning, AI algorithms, data visualization, and predictive modeling. With a focus on practical applications and hands-on projects, you will gain the expertise to analyze complex data sets and implement AI solutions effectively.
Located in the vibrant area of Malviya Nagar, Delhi, our course offers a dynamic learning environment with expert instructors who provide personalized guidance and support. By enrolling in this program, you'll learn how to leverage advanced analytics and AI to drive business decisions and innovation.

Key Highlights:

Cutting-Edge Curriculum: Our course provides in-depth training in both data analytics and artificial intelligence, covering advanced topics such as machine learning, neural networks, and big data analysis.
Practical Experience: Engage in real-world projects and case studies that allow you to apply theoretical knowledge to practical scenarios, developing skills that are directly relevant to industry needs.
Expert Instructors: Learn from experienced professionals who offer personalized mentoring and insights, helping you to master complex concepts and technologies in data analytics and AI.
Career Advancement: Completing our Advanced Data Analytics and Artificial Intelligence course will enhance your resume and open doors to exciting career opportunities in data science, AI development, and more.

Enroll in NIPSTec’s Advanced Data Analytics and Artificial Intelligence Course in Malviya Nagar today and take the next step towards becoming a leader in the rapidly evolving fields of data and AI.

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Key Learnings -Advanced Data Analytics 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.

NumPy Module

Environment Setup & Ndarray.

Array From Existing Data.

Arrays within the numerical range.

Broadcasting & Array Iteration.

Bitwise Operators, String & Mathematical Functions.

Statistical Functions, Sorting & Searching.

Pandas Module

Series, Series.map(), Series.std() & Series.value_counts().

DataFrame, DataFrame.append() & DataFrame.apply().

DataFrame.apply() & DataFrame.count().

DataFrame.iterrows() & DataFrame.merge().

DataFrame.pivot_table() & DataFrame.sort().

DataFrame.query() & DataFrame.shift().

Matplotlib and seaborn Module

Line graph.

Bar graph.

Pie Chart.

Histogram.

Scatter plot.

Visualize Distributions With Seaborn.

Plotting a Distplot.

Plotting a Distplot Without the Histogram.

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, Query References and 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).

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.

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