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Data Analytics Course in OMR

(1538)
Live Online & Classroom Training
EMI
0% Interest

Enroll in our Data Analytics Training in OMR at SLA Institute, where you’ll gain essential skills to analyze and interpret complex data sets. Our course covers key topics like data visualization, statistical analysis, and predictive modeling, equipping you with the tools needed for informed business decisions. At SLA Institute, we emphasize hands-on learning with real-world data projects, expert instruction, and personalized support. With our Data Analytics Course in OMR with 100% Placement Support, you’re well-prepared to secure a rewarding job in the high-demand field of data analytics. Whether you’re new to the field or looking to enhance your skills, SLA Institute is your partner in building a successful, data-driven career

At SLA Institute, we guarantee placement in a high-paying Developer job with the support of our experienced placement officers. Our Data Analytics Course in OMR Syllabus covers all essential topics, providing you with a comprehensive understanding of Data Analytics development.

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Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
April 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
April 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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Quick Enquiry

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 Course in OMR

The Data Analytics Course in OMR aims to achieve several goals to help you become skilled in web development using Data Analytics. Throughout the course, you’ll:

  • Equip students with a solid understanding of data analysis techniques and tools.
  • Develop skills in data visualization, enabling clear communication of insights.
  • Teach statistical methods and predictive modeling for data-driven decision-making.
  • Provide hands-on experience with real-world data projects to enhance practical knowledge.
  • Prepare students for various roles in the data analytics field through comprehensive training.

Future Scope of Data Analytics Course in OMR

Increasing Demand Across Industries

The Data Analytics Course in OMR offers numerous career opportunities as more industries rely on data for decision-making. Graduates can find jobs in fields like finance, healthcare, retail, and technology, where data analytics plays a key role in driving success.

Staying Ahead with New Technologies

Data analytics is rapidly advancing with new technologies like big data, machine learning, and AI. This course prepares you to use these tools effectively, making you a valuable asset in any organization and keeping you up to date with the latest industry trends.

High-Earning Job Prospects

Professionals in data analytics are in high demand and often earn competitive salaries. The Data Analytics Course in OMR equips you with the skills needed for well-paying jobs such as Data Analyst, Business Analyst, or Data Scientist, ensuring you are ready to succeed in these roles.

Strong Career Growth Potential

A solid foundation in data analytics opens doors to long-term career growth. As you gain more experience, you can advance to higher-level positions like Data Strategy Manager or Chief Data Officer. The future in data analytics is promising, offering a fulfilling career with ongoing learning and development opportunities.

Achieve Your Goals With SLA

SLA builds your future with comprehensive coursework and unparalleled placement support.
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Data Analytics Course Syllabus

Download Syllabus

Join our Data Analytics Course in OMR to become proficient in analyzing and managing large datasets. Begin with the basics and progress to advanced techniques for implementing effective data analytics solutions. At SLA Institute, you’ll get hands-on experience through real-world projects, learn from industry experts, and receive comprehensive job placement support. This course is designed to launch your career in Data Analytics, equipping you with the necessary skills and opportunities for success in this rapidly expanding field. Enroll now to gain the knowledge and practical experience needed to excel in Data Analytics!

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 1Loan Default Prediction

Loan Default Prediction: Analyze a financial institution’s historical loan data to predict the likelihood of loan defaults. Use data cleaning, feature selection, and machine learning models like random forests or neural networks. This project helps in risk assessment, aiding banks in making informed lending decisions.

Project 2Energy Consumption Forecasting

Work with power consumption datasets to predict future energy usage. Use time series analysis techniques, such as ARIMA or LSTM models, to identify consumption patterns. This project assists in managing energy production and optimizing resource distribution for utility companies.

Project 3E-commerce Product Recommendation

Use customer browsing and purchase history from an e-commerce platform to build a recommendation system. Implement collaborative filtering and content-based algorithms to suggest relevant products, enhancing the shopping experience and boosting sales.

Project 4Fraud Detection in Credit Card Transactions

Analyze credit card transaction data to detect fraudulent activities. Use data preprocessing techniques and train models like decision trees or support vector machines to identify suspicious transactions. This project is crucial for financial security and fraud prevention strategies.

Prerequisites for learning Data Analytics Course in OMR

To join our Data Analytics Training in OMR at SLA Institute, no specific prior knowledge is required. Whether you’re new to programming or have some experience, everyone is welcome. However, having a foundational understanding of the following can be beneficial:

  • Basic understanding of mathematics: A grasp of basic statistics and algebra is beneficial.
  • Familiarity with computers: Comfort with using software applications and basic computer skills are important.
  • Logical thinking: The ability to think critically and solve problems logically is key in data analysis.
  • Interest in data: A curiosity about working with data and finding patterns will make learning more engaging.
  • Willingness to learn: A positive attitude towards learning new tools and techniques is essential for success in the course.

Our Data Analytics Course in OMR is ideal to:

  • Students eager to excel in Data Analytics 
  • Professionals considering transitioning to Data Analytics careers
  • IT professionals aspiring to enhance their Data Analytics skills
  • Data Analysts enthusiastic about expanding their expertise
  • Individuals seeking opportunities in Data Analytics

Job Profile in Data Analytics Course in OMR

In the Data Analytics Course in OMR at SLA Institute, participants acquire crucial skills for managing and deploying cloud solutions using Data Analytics. These skills prepare them for a range of career opportunities in data science, from examining to managing complex data.

Data Analyst

  • Responsibilities: Examine and interpret complex data to help businesses make informed decisions. Create reports and use statistical tools to identify trends.
  • Salary: ₹4-8 lakhs per year.

Business Analyst

  • Responsibilities: Connect business needs with IT solutions. Gather requirements, analyze business processes, and develop strategies to improve efficiency and profitability.
  • Salary: ₹5-10 lakhs per year.

Data Scientist

  • Responsibilities: Build and apply models to understand and predict data trends. Use machine learning and statistical methods to provide insights for strategic decisions.
  • Salary: ₹8-15 lakhs per year.

Data Engineer

  • Responsibilities: Create and manage systems for collecting, storing, and processing data. Ensure data infrastructure is reliable and scalable for analysis.
  • Salary: ₹7-14 lakhs per year.

Data Visualization Specialist

  • Responsibilities: Design visual reports and dashboards to present data clearly. Use tools like Tableau or Power BI to make complex information easy to understand.
  • Salary: ₹5-10 lakhs per year.

Marketing Analyst

  • Responsibilities: Analyze marketing data to assess the effectiveness of campaigns and market trends. Use insights to improve marketing strategies and customer engagement.
  • Salary: ₹4-8 lakhs per year.

These roles provide varied opportunities in the tech industry, each needing specific data analysis skills. Completing the Data Analytics Course in OMR gives you the essential knowledge to excel in these positions and advance your career in data analytics.

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 is Data Analytics?

Data Analytics is about examining large amounts of data to find patterns and insights. It involves collecting, cleaning, and analyzing data to help businesses make better decisions and solve problems using statistics and data visualization.

Is Data Analytics easy or hard?

Data Analytics can be both easy and hard. It’s easy if you have a strong understanding of data tools and concepts. However, it can be challenging if you’re new to data analysis or complex data problems, requiring practice and skill development.

What is Data Analytics used for?

Data Analytics helps analyze and understand data to find trends, make predictions, and guide business decisions. It allows companies to see patterns, improve processes, and solve problems by turning data into useful insights.

Is coding required for Data Analytics?

Yes, coding is often required for data analytics. Knowing programming languages like Python or R helps you manipulate data, run analyses, and create visualizations. While some tools offer user-friendly interfaces, coding skills allow for more advanced and flexible data handling.

Does SLA Institute have HR personnel?

Yes, SLA Institute has HR personnel who will look into students issues and grievances.

Does SLA Institute support EMI options?

Yes, SLA Institute supports EMI options with 0% interest.’

Is Data Analytics a good career?

Yes, Data Analytics is a good career. It offers high demand, good salaries, and diverse job opportunities. Analysts help organizations make data-driven decisions, making the role valuable in many industries.

Does SLA Institute provide Lifetime Placement Support?

Yes, SLA Institute provides Lifetime Placement Support to assist students in securing job placements throughout their careers.

On Average Students Rated The Data Analytics Course 4.70/5.0
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