Data Analytics Mastery Program

  • High Demand In Business And It: Data analytics is in demand because companies need professionals who can analyze data, build dashboards, create reports, and support business decisions.
  • Useful For Multiple Backgrounds: Data analytics is suitable for learners from commerce, management, computer science, and non-technical backgrounds because it focuses on practical data understanding.
  • Build Strong Business Insight Skills: Learners understand how to convert raw data into meaningful dashboards, reports, trends, kpis, and actionable business recommendations.
4 Months ₹37,999 ₹29,999
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Data Analytics Mastery Program
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Course Overview

Data Analytics is the process of collecting, cleaning, analyzing, visualizing, and interpreting data to support better business decisions. This 4 months course helps learners build advanced analytics skills using Excel, SQL, Python, statistics, data cleaning, Power BI, dashboard development, KPI reporting, business intelligence, predictive analytics basics, and industry-level analytics projects.

Course with Live Project

No Refund Available

advanced analytics tool training: learners work with excel, sql, python, pandas, numpy, statistics, and power bi to analyze business data professionally.

dashboard, kpi, and bi reporting: the course focuses on excel dashboards, power bi reports, kpi tracking, interactive visuals, data modeling basics, and business intelligence reporting.

Industry-level Analytics Projects: Develop Practical Projects Like Hospital Analytics Systems, Retail Chain Kpi Dashboards, Hr Performance Analytics, E-commerce Analytics, And Financial Reporting Dashboards.

Course Content

  • • understanding data analytics fundamentals
  • • exploring types of data analytics
  • • learning data analytics lifecycle
  • • understanding data-driven decision making
  • • exploring business intelligence concepts
  • • understanding real-world data applications
  • • learning analytics career opportunities
  • • setting up analytics environment

  • • understanding excel interface basics
  • • cleaning and formatting raw data
  • • using logical excel functions efficiently
  • • working with lookup functions in excel
  • • applying vlookup and xlookup usage
  • • creating pivot tables and pivot charts
  • • using conditional formatting techniques
  • • building interactive excel dashboards
  • • performing data analysis in excel
  • • tracking kpis using excel reports
  • • creating business summary reports

  • • understanding database management concepts
  • • writing basic to advanced sql queries
  • • filtering and sorting data efficiently
  • • using aggregate functions in sql
  • • working with group by statements
  • • understanding sql joins and relationships
  • • using subqueries for data analysis
  • • working with window functions
  • • creating analytical sql reports
  • • solving real-world business queries

  • • learning python programming basics
  • • understanding variables and data types
  • • working with conditional statements
  • • using loops for data processing tasks
  • • creating functions and modules
  • • handling files and data inputs
  • • managing errors and exceptions
  • • using python libraries for analytics
  • • writing efficient python code

  • • understanding numpy array operations
  • • performing mathematical data operations
  • • using pandas for data analysis
  • • working with dataframes efficiently
  • • reading csv and excel files
  • • cleaning and transforming datasets
  • • handling missing values properly
  • • performing exploratory data analysis
  • • extracting business insights from data

  • • understanding mean median mode concepts
  • • learning variance and standard deviation
  • • understanding probability in analytics
  • • exploring correlation and relationships
  • • identifying trends in data
  • • understanding data distribution patterns
  • • applying statistical thinking
  • • forecasting basic data trends

  • • understanding data visualization concepts
  • • creating charts and graphs
  • • using matplotlib visualization library
  • • using seaborn statistical visualizations
  • • building correlation heatmaps
  • • creating dashboard visual reports
  • • presenting data insights clearly
  • • designing professional reports

  • • understanding power bi interface
  • • importing and transforming data
  • • creating interactive dashboards
  • • using power query editor tools
  • • building data models in power bi
  • • using dax functions basics
  • • creating kpi dashboards
  • • publishing and sharing reports
  • • creating business intelligence reports

  • • understanding end-to-end analytics process
  • • cleaning real-world business data
  • • performing exploratory data analysis
  • • identifying business trends and patterns
  • • creating insightful reports
  • • supporting data-driven decisions

  • • understanding predictive analytics concepts
  • • learning forecasting techniques
  • • identifying business trends
  • • understanding regression basics
  • • predicting future outcomes
  • • supporting business decisions

  • • advanced dashboard optimization
  • • kpi performance monitoring task
  • • data modeling exercise
  • • power bi dax functions task
  • • real business case analysis
  • • strategic data reporting

  • • hospital analytics system
  • • retail chain kpi dashboard
  • • hr performance analytics platform

Skills Developed with Data Analytics Course

Excel Analytics: Learn advanced excel formulas, logical functions, lookup functions, pivot tables, pivot charts, conditional formatting, and dashboard creation.
Sql For Data Analysis: Work with sql queries, filtering, sorting, aggregate functions, group by, joins, subqueries, window functions, and analytical reports.
Python For Analytics: Learn python fundamentals, data structures, functions, file handling, automation, and analytics-based coding tasks.
Pandas And Numpy: Practice dataframe handling, csv and excel files, filtering, sorting, grouping, merging, missing value handling, and data transformation.
Statistics For Analytics: Understand mean, median, mode, variance, standard deviation, probability, correlation, trend analysis, and forecasting basics.
Data Cleaning And Preparation: Work with missing values, duplicate records, incorrect formats, inconsistent data, outliers, and clean dataset preparation.
Data Visualization: Create charts, graphs, line plots, bar charts, pie charts, heatmaps, dashboards, and professional visual reports.
Power Bi Dashboard Development: Learn data import, power query, data modeling basics, kpi cards, slicers, filters, visuals, dax basics, and report publishing.
Business Intelligence Reporting: Prepare sales reports, customer reports, hr reports, financial summaries, kpi dashboards, and business insight documents.
Analytics Project Development: Practice cleaning data, analyzing datasets, creating dashboards, generating insights, documenting findings, and presenting industry-level reports.

Career Opportunities after Data Analytics Course

This course opens doors to multiple high-demand career paths across industries.

Data Analyst:

Clean, analyze, visualize, and report data to help organizations make better business decisions.

Business Intelligence Analyst:

Build dashboards, kpi reports, data models, and business intelligence visuals using tools like power bi, sql, and excel.

Reporting Analyst:

Prepare business reports, performance summaries, financial reports, and data-driven insights for management teams.

Power Bi Developer:

Create interactive dashboards, kpi cards, dax calculations, data models, and business intelligence reports.

Sql Data Analyst:

Work with databases, write queries, extract business data, and prepare structured analytical reports.

Why Enroll in Data Analytics with Solitaire Learning?

Then Moves Toward Python, Power Bi, Dashboards, Bi Reporting, And Analytics Projects.
Industry-level Dashboard Practice: Learners work on real-world projects like hr analytics, hospital analytics, retail kpi dashboards, e-commerce analytics, and financial reports.
Industry-relevant Tools: The course covers excel, sql, python, pandas, numpy, power bi, dax basics, charts, dashboards, and reporting tools.
Mentor-guided Project Support: Learners receive mentor support for data cleaning, dashboard creation, report building, analytics projects, portfolio preparation, and interview practice.
Strong Career And Portfolio Preparation: The course helps learners become job-ready by covering data analysis, business reporting, dashboard development, bi tools, and industry-level projects.
Frequently Asked Questions

Have Questions About This Course?

Find answers to the most common questions learners ask before enrolling.

No, beginners can also join the training program without prior coding knowledge. Concepts are taught step-by-step from basics.

Basic analytical thinking and simple statistics understanding are sufficient for learning Data Analytics. Advanced mathematics is not compulsory for beginners.

Yes, students from any educational background or stream can learn Data Analytics. The course is designed for both technical and non-technical learners.

A laptop with minimum 8GB RAM, i3/i5 processor, and stable internet connection is recommended for smooth practical work and dashboard development.

No, Excel basics are covered during the training program. Students gradually learn advanced formulas, reporting, and dashboard techniques.

Data Analytics is the process of collecting, analyzing, and interpreting data to identify patterns, generate reports, and support better business decisions. It helps organizations improve performance and understand customer behavior.

Students learn Excel, SQL, Python, Power BI, statistics, dashboard development, KPI reporting, and business reporting techniques. The course focuses on both analytical concepts and practical implementation.

Yes, students learn dashboard creation, data visualization, report publishing, and KPI tracking using Power BI. Practical business dashboard projects are also included.

Yes, SQL is covered from basic to advanced level with practical query exercises and reporting tasks. Students learn database handling and data extraction techniques.

Yes, students work with real-world business datasets and reporting scenarios. This helps learners understand practical analytics workflows and business problem-solving.

Yes, students create analytics dashboards, business reports, KPI dashboards, and visualization projects using Excel and Power BI. These projects help build strong portfolios.

Yes, Python is included for automation, data analysis, and advanced analytics tasks. Students learn libraries like Pandas, NumPy, and Matplotlib for data processing.

Yes, every module contains assignments, dashboard tasks, SQL exercises, and reporting activities. Regular practice helps improve analytical and reporting skills.

Yes, Data Analytics is a highly in-demand field with opportunities in business intelligence, reporting, marketing analytics, and data-driven decision-making roles.

Yes, students receive project guidance, portfolio support, and certification after successful completion of the training. Career guidance and interview preparation are also included.
Course FAQ

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