Software Training Institute in Chennai with 100% Placements – SLA Institute

Easy way to IT Job

Data Analytics Online Course

(1987)
Live Online & Classroom Training
EMI
0% Interest

Our Data Analytics Online Training will make students learn some of the most in-demand concepts in Data Analytics such as – Core Python, Power BI, Power Query, M Language, Data Modeling, DAX, report Views, Visualizations, Advanced Pandas Service etc. This curriculum will surely make students experts in the concept of  Data Analytics in a shorter span of time. Our Data Analytics Online Course with 100% placement support is curated with the help of leading experts from the IT industry, which makes our Data Analytics Online Course up-to-date in accordance with the latest trends.

Our SLA Institute is guaranteed to place you in high-paying Data Analyst and other Data Analytics related jobs with help of our experienced placement officers. SLA Institute’s Course Syllabus for Data Analytics covers all topics that are guaranteed to give you a complete understanding of the Data Analytics Online Course.

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Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
March 2025
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
March 2025
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

Course Fee

Save up to 20% in your Course Fee on our Job Seeker Course Series

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Placement

100% Assistance

Learning

Job-Centered Approach

Timings

Convenient Hrs

Mode

Online & Classroom

Certification

Industry-Accredited

This Course Includes

  • FREE Demo Class
  • 0% EMI Loan Facilities
  • FREE Softskill & Placement Training
  • Tie up with more than 500+ MNCs & Medium Level Companies
  • 100% FREE Placement Assistance
  • Course Completion Certificate
  • Training with Real Time Projects
  • Industry-Based Coaching By MNC IT Professionals
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Expected Criteria for Assured Placement

The following criteria help the placement team guide the candidates to get placed immediately after the course completion through SLA Institute.

  • 80% of coursework completion helps us arrange interviews in required companies.
  • 2 or 3 projects to be done for the selected course to ace the technical round effectively.
  • Ensure attending the placement training right from the first day of the selected course.
  • Practice well with resume building, soft skill, aptitude skill, and profile strengthening.
  • Utilize the internship training program at SLA for the complete technical skills.
  • Collect the course completion certificate and update the copy to the placement team.
  • Ensure your performance indicator meets the expectation of top companies.
  • Always be ready with the updated resume that includes project details done at SLA.
  • Enjoy unlimited interview arrangements along with internal mock interviews.
Have Queries? Ask our Experts

+91 89256 88858

SLA's Distinctive Placement Approach

1

Tech Courses

2

Expert Mentors

3

Assignments & Projects

4

Grooming sessions

5

Mock Interviews

6

Placements

Objectives of Data Analytics Online Course

The primary objective of our Data Analytics Online Course is to make enrolled candidates experts in Data Analytics. This Data Analytics Online Course will make students grow into successful and most in-demand Data Analysts, and more. SLA Institute’s Data Analytics Online Course Curriculum is loaded with some of the most useful and rare concepts that will surely give students a complete understanding of Data Analytics. So, some of those concepts are discussed below:

  • To make students well-versed with fundamental topics of Data Analytics like – Core Python, Power BI, Power Query – Data Transformation, Benefits of Data Transformation, Shape or Transform Data using Power Query, Overview of Power Query / Query Editor etc. 
  • To make students learn more about Data Analytics through topics like – M Language, Data Modeling – AutoDetect the relationship, Create a new relationship, Edit existing relationships etc.
  • To give students knowledge about the advanced topics in Data Analytics through topics like – Power BI Cloud Architecture, Creating Power BI Service Account, SIGN IN to Power BI Service Account, converting CSV files to data frames, converting dataframes to CSV files, converting dataframes to excel file etc.

Scopes in the future for Data Analytics Online Course

The following are the scopes available in the future for the Data Analytics Online Course:

  • Advanced Analytics Methods: Courses are likely to continue focusing on sophisticated techniques such as machine learning, artificial intelligence, and predictive analytics. As these technologies become integral to business operations, mastering them will be essential.
  • Specialized Tracks: There is a growing trend of online courses offering specialized tracks in areas such as healthcare, finance, marketing, and sports analytics. These specialized programs are designed to help learners apply data analytics skills to particular sectors.
  • Big Data Technologies Integration: With the rise of big data technologies like Hadoop, Spark, and cloud-based analytics platforms, courses will increasingly include practical training on managing and analyzing large-scale data sets.
  • Real-Time Analytics: The emphasis on real-time analytics and streaming data will increase, reflecting the need for businesses to make decisions based on up-to-date information.

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Data Analytics Course Syllabus

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SLA Institute’s Data Analytics Online Course Syllabus comes with 100% placement support so students will be guaranteed a placement in an esteemed organization. In addition to that, the Data Analytics Online Course Syllabus is also carefully curated with the help of leading professionals and experts from the IT industry with so many hours invested in it. So, everything that our students learn in the Data Analytics Online course is fully up-to-date to the current trends in the IT industry, which increases their chances of getting employed.

CORE PYTHON
  • Python Introduction & history
  • Color coding schemes
  • Salient features & flavors
  • Application types
  • Language components
  • String handling management
    • String operations – indexing, slicing, ranging
    • String methods – concatenation, repetition, formatting
    • Supporting functions
  • Native data types
    • List
    • Tuple
    • Set
    • Dictionary
  • Decision making statements
    • If
    • If…else
    • If…elif…else
  • Looping statements
    • For loop
    • While loop
  • Function types
    • Built-in functions
    • Math functions
    • User defined functions
    • Recursive functions
    • Lambda functions
  • OOPs
    • Classes and objects
    • init constructor
    • Self-keyword
    • Data abstraction
    • Data encapsulation
    • Polymorphism
    • Inheritance
  • Exception handling
    • Error vs exception
    • Types of error
    • User defined exception handling
    • Exception handler components
    • Try block, except block, finally block
POWER BI INTRODUCTION
  • Data Visualization
  • Reporting Business Intelligence (BI)
  • Traditional BI
  • Self-Serviced BI Cloud Based BI
  • On Premise BI
  • Power BI Products
  • Power BI Desktop (Power Query, Power Pivot, Power View)
  • Flow of Work in Power BI Desktop
  • Power BI Report Server
  • Power BI Service, Power BI Mobile
  • Power BI Architecture
  • A Brief History of Power BI
POWER QUERY
  • Data Transformation
  • Benefits of Data Transformation
  • Shape or Transform Data using Power Query
  • Overview of Power Query / Query Editor
  • Query Editor User Interface
  • The Ribbon (Home, Transform, Add Column, View Tabs)
  • The Queries Pane
  • The Data View / Results Pane
  • The Query Settings Pane, Formula
  • Bar Saving the Work
  • Data types
  • Changing the Data type of a Column Filters in Power Query
  • Auto Filter / Basic Filtering Filter a Column using
  • Text Filters Filter a Column using Number Filters
  • Filter a Column using Date Filters Filter Multiple Columns
  • Remove Columns / Remove Other Columns Name
  • Rename a Column Reorder Columns or Sort Columns
  • Add Column / Custom Column Split Columns Merge
  • Columns PIVOT, UNPIVOT Columns Transpose Columns
  • Header Row or Use First Row as Headers Keep Top Rows
  • Keep Bottom Rows Keep Range of Rows Keep Duplicates
  • Keep Errors Remove Top Rows
  • Remove Bottom Rows
  • Remove Alternative Rows
  • Remove Duplicates, Remove Blank Rows
  • Remove Errors Group Rows / Group By
M LANGUAGE
  • IF..ELSE Conditions
  • TransformColumn()
  • RemoveColumns()
  • SplitColumns()
  • ReplaceValue()
  • Table.Distinct() Options and GROUP BY Options
  • Table.Group()
  • Table.Sort() with Type Conversions
  • PIVOT Operation and Table.Pivot ().
  • List Functions Using Parameters with M Language
DATA MODELING
  • Data Modeling Introduction Relationship
  • Need of Relationship Relationship Types
  • Cardinality in General
    • One-to-One
    • One-to-Many
    • Many-to-One
    • Many-to-Many
  • AutoDetect the relationship
  • Create a new relationship
  • Edit existing relationships
  • Make Relationship Active or Inactive
  • Delete a relationship
DAX
  • What is DAX
  • Calculated Column, Measures
  • DAX Table and Column Name Syntax
  • Creating Calculated Columns
  • Creating Measures
  • Calculated Columns Vs Measures
  • DAX Syntax & Operators
  • Types of Operators
    • Arithmetic Operators
    • Comparison Operators
    • Text Concatenation Operator
    • Logical Operators
DAX FUNCTIONS TYPES
  • Date and Time Functions
    • YEAR, MONTH,DAY
    • WEEKDAY, WEEKNUM FORMAT (Text Function)
    • Month Name, Weekday Name
    • IF
    • TRUE, FALSE NOT,
    • OR, IN, AND
  • Text Function
    • LEN, CONCATENATE
    • LEFT, RIGHT, MID UPPER
    • LOWER TRIM, SUBSTITUTE, BLANK
  • Logical Functions
    • IF TRUE, FALSE NOT
    • OR, IN, AND IF ERROR SWITCH
  • Math & Statistical Functions
    • INT ROUND, ROUNDUP
    • ROUNDDOWN
    • DIVIDE EVEN, ODD
    • POWER, SIGN SQRT
    • FACT SUM, SUMX MIN, MINX MAX
    • MAXX COUNT,
    • COUNTX AVERAGE
    • AVERAGEX COUNTROWS
    • COUNTBLANK
REPORT VIEW
  • Report View User Interface
  • Fields Pane
  • Visualizations pane
  • Ribbon, Views, Pages Tab
  • Canvas Visual Interactions Interaction Type (Filter, Highlight, None)
  • Visual Interactions Default Behavior, Changing the Interaction
  • Grouping and Binning Introduction
  • Using grouping, Creating Groups on Text Columns
  • Using binning, Creating Bins on Number Column and Date Columns
  • Sorting Data in Visuals
  • Changing the Sort Column
  • Changing the Sort Order
  • Sort using column that is not used in the Visualization
  • Sort using the Sort by Column button
  • Hierarchy Introduction
  • Default Date Hierarchy
  • Creating Hierarchy
  • Creating Custom Date Hierarchy
  • REPORT VIEW
  • Change Hierarchy Levels
  • Drill-Up and Drill-Down Reports
  • Data Actions, Drill Down, Drill Up, Show Next Level
  • Expand Next Level Drilling filters other visuals option
VISUALIZATIONS
  • Visualizing Data
  • Why Visualizations
  • Visualization types
  • Create and Format Bar and Column Charts
  • Create and Format Stacked Bar Chart
  • Stacked Column Chart
  • Create and Format Clustered Bar Chart
  • Clustered Column Chart
  • Create and Format 100% Stacked Bar Chart 100% Stacked Column Chart
  • Create and Format Pie and Donut Charts
  • Create and Format Scatter Charts
  • Create and Format Table Visual
  • Matrix Visualization
  • Line and Area Charts
  • Create and Format Line Chart, Area Chart
  • Stacked Area Chart Combo Charts
  • VISUALIZATIONS
  • Create and Format Line and Stacked Column Chart
  • Line and Clustered Column Chart
  • Create and Format Ribbon Chart
  • Waterfall Chart, Funnel Chart
POWER BI SERVICE
  • Power BI Service Introduction
  • Power BI Cloud Architecture
  • Creating Power BI Service Account
  • SIGN IN to Power BI Service Account
  • Publishing Reports to the Power BI service
  • Import / Getting the Report to PBI Service
  • My Workspace / App Workspaces Tabs
  • DATASETS, WORKBOOKS, REPORTS & DASHBOARDS
  • Working with Datasets Creating Reports in Cloud using Published
  • Datasets
  • Creating Dashboards Pin Visuals and Pin LIVE
  • Report Pages to Dashboard
  • Advantages of Dashboards Interacting with
  • Dashboards
  • Formatting Dashboard, Sharing Dashboard
ADVANCED PANDAS FUNCTIONS
  • Group by()
  • Pivot tables()
  • Multi-indexing()
  • merge()
  • concatenate()
  • join()
  • data transformation using apply()
  • map()
  • query()
  • Resampling time series functionality
  • excel writer()
  • pipe()
  • creating dataframes
  • reading CSV files with intrinsic index
  • converting CSV files to dataframes
  • converting dataframes to CSV files
  • converting dataframes to excel file
ADVANCED SQL FUNCTIONS
  • Common Table Expressions (CTE)
  • Recursive CTE’s
  • temporary functions
  • pivoting data with sum() and CASE WHEN
  • Except vs Not in
  • self joins, rank vs dense_rank vs row number
  • ranking data
  • calculating delta values,
  • multiple groupings using rollup
  • calculating running totals
  • computing a moving average
  • date time manipulations
  • Formatting strings, stored methods
  • JOINS
  • Sub Queries
  • Manipulation of date and time
  • procedural data storage
  • Connecting SQL to Python or R language, window Functions
PROJECT
  • Project1 – Product Sales Analysis – Power BI Project and review
  • Project2 – Financial Performance Analysis – Power BI Project and review
  • Project3 – Health care sales Analysis –
  • Intermediate Power BI project and review
  • Project4 – Anamoly detection in Credit card transactions – Intermediate Power BI project and review

Project Practices on Data Analytics Training

Project 1Sales Forecasting

Examine historical sales data to predict future sales trends. Implement time series analysis and machine learning techniques to forecast sales and assess the impact of variables like seasonality, promotions, and market conditions.

Project 2Customer Segmentation

Use clustering methods (e.g., K-means, hierarchical clustering) to group customers based on purchasing behaviors, demographics, or other characteristics. Generate actionable insights for targeted marketing and personalized customer interactions.

Project 3Churn Analysis

Investigate customer data to uncover patterns and factors leading to customer churn. Create predictive models to estimate which customers are likely to leave and recommend strategies for retention.

Project 4Social Media Analytics

Gather and analyze social media data to uncover trends, sentiment, and engagement levels. Apply text mining and sentiment analysis to evaluate brand perception and guide marketing strategies.

Prerequisites for learning Data Analytics Online Course

SLA Institute does not demand any prerequisites for any course at all. SLA Institute has courses that cover everything from the fundamentals to advanced topics so whether the candidate is a beginner or an expert they will all be accommodated and taught equally in SLA Institute. However having a fundamental understanding of these concepts below will help you understand Data Analytics better, However it is completely optional:

  • Fundamental Mathematics and Statistics: A basic grasp of mathematics, especially statistics, is important. Key areas include probability, descriptive statistics, and inferential statistics.
  • Proficiency with Spreadsheets: Skills in using spreadsheet software such as Microsoft Excel or Google Sheets are often useful. Being able to handle data, create formulas, and utilize functions is a valuable part of data analytics.
  • Basic Programming Knowledge: Although not always mandatory, having some familiarity with programming languages like Python or R can be advantageous, as these are frequently used for data manipulation and analysis in many courses.
  • Database Knowledge: Understanding the basics of databases and SQL (Structured Query Language) is helpful. Knowing how to query and manage data in databases is a common aspect of data analytics.

Our Data Analytics Online Course is fit for:

  • Students eager to excel in Analytics
  • Professionals considering transitioning to Data Analytics careers
  • IT professionals aspiring to enhance their Data Analytics skills
  • Data Analysts who are looking forward to expanding their expertise.
  • Individuals seeking opportunities in the Data Analytics field.

Job Profile for Data Analytics Online Course

After finishing the Data Analytics Online Course, students will be placed in various organizations through SLA Institute. This section will explore the various range of job profiles in which students can possibly be possible be placed as in the Data Analytics sector; 

  • Data Analyst: Data Analytics Online Course will train students into successful Data Analysts who will analyze complex datasets, generate reports, and offer insights to assist organizations in decision-making.
  • Data Scientist: Data Analytics Online Course will turn students into skilled Data Scientists who develop and apply predictive models, conduct advanced statistical analysis, and extract actionable insights from data.
  • Business Intelligence (BI) Analyst: Data Analytics Online Course will make students into Business Intelligence (BI) Analyst who will create and implement BI solutions, analyze business data to support decisions, and develop dashboards and reports.
  • Data Engineer: The SLA Institute will provide students with enough resources that it will train students into successful Data Engineer who will be doing database management, ETL processes, programming (Python, Java), and big data technologies (e.g., Hadoop, Spark).
  • Quantitative Analyst: The SLA Institute will turn students into skilled Quantitative Analysts who will utilize mathematical and statistical models to analyze financial data, guiding investment strategies and risk management.
  • Data Architect: The SLA Institute will make students into Data Architect who will design and oversee data systems and architectures to support integration and governance.
  • Machine Learning Engineer: Create and deploy machine learning models to address complex issues and enhance processes.
  • Marketing Analyst: Examine marketing data to assess campaign effectiveness, understand customer behavior, and track market trends.
  • Operations Analyst: Analyze operational data to refine business processes, improve performance, and cut costs.
  • Healthcare Data Analyst: Analyze healthcare data to enhance patient outcomes, optimize treatments, and support healthcare decisions.
  • Product Analyst: Assess product performance, analyze user behavior, and provide insights to drive product strategy and development.
  • Financial Analyst: Examine financial data to aid in investment decisions, budget planning, and financial forecasting.

Want to learn with a personalized course curriculum?

The Placement Process at SLA Institute

  • To Foster the employability skills among the students
  • Making the students future-ready
  • Career counseling as and when needed
  • Provide equal chances to all students
  • Providing placement help even after completing the course

Data Analytics Course FAQ

What distinguishes SQL from NoSQL databases, and when should each be used?

SQL databases are relational, using structured schemas and supporting complex queries and transactions, making them ideal for consistent data integrity and complex relationships (e.g., MySQL, PostgreSQL). NoSQL databases are non-relational with flexible schemas, suited for handling high volumes of unstructured or semi-structured data and providing scalability and performance (e.g., MongoDB, Cassandra). Use SQL for structured, transactional data and NoSQL for large-scale, flexible data needs.

What are effective methods for handling missing values in datasets?

Common techniques include:

  • Imputation: Replacing missing values with estimates like the mean, median, or mode.
  • Interpolation: Estimating missing values using surrounding data, especially in time series.
  • Deletion: Removing rows or columns with missing values if minimal.
  • Model-based Methods: Predicting missing values using machine learning models based on other features.

The choice depends on the data type and extent of missing values.

What are the differences between supervised and unsupervised machine learning, and when is each used?

Supervised learning uses labeled data to train models for prediction tasks, such as classification and regression (e.g., predicting customer churn). Unsupervised learning works with unlabeled data to find patterns or groupings, used for exploratory analysis like clustering and dimensionality reduction (e.g., customer segmentation). Use supervised learning for tasks with known outcomes and unsupervised for pattern discovery in unlabeled data.

What challenges are associated with big data, and how can they be managed?

Key challenges include:

  • Scalability: Handled with distributed computing frameworks like Hadoop or Spark.
  • Data Quality: Managed through data cleaning and validation processes.
  • Storage: Addressed with cloud-based solutions and scalable storage.
  • Performance: Improved by optimizing queries and using efficient algorithms.

These issues are mitigated using specialized tools and best practices for managing and processing large datasets.

Where is the corporate office of the SLA Institute located?

The SLA Institute’s corporate office is located at the K.K.Nagar branch.

Is there an EMI option available in the SLA Institute?

Yes, the SLA Institute has an EMI option available with 0% interest. 

Is it easy to learn the Data Analytics Online Course?

Learning the Data Analytics Online Course will be easy if students cooperate with trainers by completing the projects within deadline. 

How long is the Data Analytics Online Course?

The Data Analytics Online Course is 2 months long, 

On Average Students Rated The Data Analytics Course 4.60/5.0
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