Best Data Analytics Course In Amravati With Placement
100% Placement Assistance | Live Online Sessions
3 Month Personalized Live Data Analytics Training is taught by industry experts in a comprehensive & question-oriented format.
Enroll Before: 01st March, 2025
2000+
Students Trained
100%
Placement Assistance
01 March, 2025
Start Date
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EMI Available
7:30 AM - 9:30 AM
Lecture Timings ( IST )
Key Highlights Of The Data Analyst Course
- 100+ Hours Of Live Class
- 50+ Hours of Videos
- 1-on-1 Mentoring Sessions
- 20+ Industry Tools Mastery
- In-Class Live Presentations
- 3+ Live Projects & Practice Assignments
- 100% Placement Assistance
- Resume & Interview Training
Syllabus for Data Analyst Course
Our hybrid online Data Analyst course is designed to teach students the basics to the advanced level concepts of Data Analyst with practice assignments and offline in-class projects which helps them to get placed in MNC’s.
Term 1
In this term, you will learn how to ace Data Analytics
- Overview of Data Analytics
- Role of a Data Analyst
- Introduction to Data-Driven Decision Making
- Tools and Technologies used in Data Analysis
Term 2
In this term, you will learn how to ace Data Analytics
- Intro to Power BI
- Data Acquisition & Transform.
- Modeling Basics
- Visualization Techniques
- Formatting & Custom VisualsÂ
- Advanced DAXÂ Â Â Â Â Â Â Â Â Â Â Â Â
- Power Query M Language
- Power BI Service             Â
- Data Refresh and Gateways   Â
- Power BI Administration    Â
- Dataflow                 Â
- Paginated Reports            Â
- Performance Optimization     Â
- Power BI Embedded        Â
- Final Project and ReviewÂ
Term 3
In this term, you will learn how to ace Data Analytics
Data Collection And Data Wrangling
- Data Sources and Types
- Data Collection Techniques
- Data Cleaning and Preprocessing
- Handling Missing Data
- Data Transformation and Normalization
- Outlier Detection and Treatment
- Introduction to Data Wrangling Tools (e.g., Excel, SQL, Python)
Descriptive Statistics and Data Visualization
- Measures of Central Tendency (Mean, Median, Mode)
- Measures of Dispersion (Range, Variance, Standard Deviation)
- Data Distribution (Normal, Skewness, Kurtosis)
- Introduction to Data Visualization
- Charts and Graphs (Bar, Pie, Line, Histogram)
- Dashboarding (using Power BI, Tableau)
Data Visualization Best Practices
Term 4
In this term, you will learn how to ace Data Analytics
- Excel Basics: Formulas, Functions, and Pivot Tables
- Data Analysis Tools in Excel
- Performing Statistical Analysis in Excel
- Creating Data Models in Excel
- Advanced Excel Functions (VLOOKUP, INDEX-MATCH)
Term 5
In this term, you will learn how to ace Data Analytics
- Introduction to Databases and SQL
- Basic SQL Queries
- SELECT, INSERT, UPDATE, DELETE
- SQL Joins and Subqueries
- Data Aggregation and Grouping
- Advanced SQL Functions (Window Functions, CTEs)
- SQL for Data Analysis: Case Studies
- Introduction to Python Programming
- Python Libraries for Data Analysis (Pandas, NumPy, Matplotlib, Seaborn)
- Data Manipulation with Pandas
- DataFrames, Series, Indexing, and Filtering
- Exploratory Data Analysis (EDA)
- Descriptive Statistics, Correlation Analysis
- Data Visualization in Python
- Creating Visualizations with Matplotlib and Seaborn
- Probability Theory
- Hypothesis Testing
- t-tests, ANOVA, Chi-Square Test
- Regression Analysis
- Simple Linear Regression
- Multiple Regression
- Logistic Regression
- Introduction to Time Series Analysis
Term 6
In this term, you will learn how to ace Data Analytics
- The Importance of Data Storytelling
- Techniques for Effective Data Communication
- Creating Reports and Dashboards
- Presenting Data Analysis Results to Stakeholders
- Real-World Data Analysis Project
- Problem Identification and Data Collection
- Data Cleaning and Analysis
- Visualization and Reporting
Presentation of Findings
- Building a Data Analyst Portfolio
- Resume Writing and Interview Preparation
- Networking and Industry Best Practices
- Job Search Strategies and Career Pathway
Join our comprehensive Data Analytics course and qualify for top industry roles, including:
Power BI Developer
- Master the art of creating interactive dashboards and reports with Power BI.
- Gain the skills to transform raw data into meaningful insights.
- Become a key player in data-driven decision-making processes.
Data Analyst
- Learn to analyze complex datasets, identify trends, and make data-driven recommendations.
- Develop expertise in tools like Excel, SQL, and Python for effective data analysis.
- Step into a role that is at the heart of business strategy and performance.
MIS Analyst
- Specialize in managing and interpreting data to improve business efficiency.
- Use tools like Excel and SQL to generate and analyze reports for management.
- Become the go-to expert for management information systems in your organization.
Process Excellence Analyst
- Focus on improving business processes through data analysis and optimization.
- Gain skills in identifying process bottlenecks and recommending solutions.
- Play a crucial role in enhancing productivity and driving organizational success.
Tools You’ll Master
Mentors
Python Trainer
4+ Years ITExperience
4+ Years Teaching Experience
Project Trainer
5+ Years IT Experience
5+Teaching Experience
Digital Marketing
3+ Years IT Experience
2+Teaching Experience
Manual Trainer
5+ Years IT Experience
5+Teaching Experience
Our Learners Work At
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Bug Spotter Reviews
Data Analytics Course in AmravatiÂ
Introduction to Data Analytics
Data Analytics involves examining raw data to extract meaningful insights that support smarter decision-making. By recognizing patterns, trends, and correlations, businesses can optimize operations, improve efficiency, and develop data-driven strategies.
Why Data Analytics Matters?
- Understand Customer Behavior: Personalize products and services.
- Optimize Operations :Â Increase efficiency and reduce costs.
- Predict Trends: Forecast market changes and consumer actions.
Key Components of Data Analytics
- Data Collection: Gathering relevant data from multiple sources such as databases, APIs, and web scraping.
- Data Cleaning: Removing errors, handling missing values, and ensuring data consistency for accurate analysis.
- Data Analysis: Utilizing tools like Excel, SQL, Python, and R to identify patterns, trends, and insights.
Data Analytics Process
- Collect Data – Gather relevant information.
- Clean & Prepare Data – Ensure data quality.
- Analyze Data – Apply statistical or machine learning techniques.
- Create Visualizations – Represent insights through charts and graphs.
- Interpret & Present Insights – Communicate findings effectively.
Role of a Data Analyst
A Data Analyst converts raw data into actionable insights using tools like Excel, SQL, Python, Tableau, and Power BI. Their expertise enables businesses to make informed, data-driven decisions for improved efficiency and growth.
Applications of Data Analytics
- Healthcare:Enhance patient care, streamline operations, and optimize resource allocation.
- E-commerce: Personalize user experiences, optimize pricing strategies, and improve product recommendations.
- Finance: Detect fraud, assess investment risks, and enhance financial decision-making.
- Marketing: Optimize campaigns, refine customer segmentation, and boost engagement through data-driven strategies.
How to Get Started
-  Enroll in Courses – Platforms like Coursera and Udemy offer structured learning.
- Gain Practical Experience – Work on real-world datasets.
- Network & Grow – Join data analytics communities fr mentorship and career guidance.
Eligibility
for Data Analyst Course​
Eligibility and Requirements for the Program
- Open to All Graduates – No specific background required. Anyone with a degree can apply.
- No Prior Coding Experience Needed – Just a willingness to learn Python, R, and SQL.
- Time Commitment – Dedicate 3-4 hours per day for lectures, hands-on practice, and self-study.
- Beginner-Friendly –Designed for those with no prior experience in data analytics.
- Curious & Analytical Mindset – A problem-solving approach will help you excel in the field..
FAQs
Frequently asked questions
1.How to Find the Best Data Analytics Course?
Selecting the right data analytics course can accelerate your learning and career growth. Consider these key factors:
- Structured Curriculum –  Covers everything from fundamentals to advanced techniques.
- Hands-on Learning –  Includes real-world projects and case studies.
- Expert Instructors – Learn from industry professionals with real-world experience.
- Career Support – Look for job placement assistance, resume building, and industry-recognized certifications.
2. Can I Teach Myself Data Analytics?
Absolutely! You can learn data analytics on your own using free online resources, tutorials, and community forums. However, structured courses provide a more organized approach, hands-on experience, and faster skill development.
3. What Are the Most Essential Tools for Data Analytics?
Data analysts use:
- Excel & SQL –For data manipulation, querying, and database management.
- Python & R – Used for statistical analysis, automation, and machine learning.
- Tableau & Power BI –  Powerful tools for data visualization and interactive reporting.
4. Why Is Data Visualization Important?
Data visualization transforms complex data into clear, easy-to-understand visuals, enabling better analysis, interpretation, and decision-making. It helps identify patterns, trends, and insights at a glance.
Modern tools like Tableau and Power BI allow users to create interactive dashboards and compelling visual reports, making data-driven storytelling more effective.
5. Is Data Analytics a Good Career Choice?
Absolutely! Data Analytics is one of the most in-demand fields today, with growing demand across industries like healthcare, finance, e-commerce, and marketing, data analytics offers strong job prospects and competitive salaries.
6. How Is Data Analytics Used in Real Life?
- Marketing: Personalizing campaigns based on customer data.
- Finance: Fraud detection and risk assessment.
- Retail: Predicting inventory needs and optimizing supply chains.
- Healthcare: Improving patient care through predictive analytics.
How much can a data analyst earn?
Data analyst salaries depend on factors like location, experience, industry, and skill set.
- Entry-Level: $50,000 – $80,000 per year (U.S.)
- Mid to Senior Level: : Higher salaries based on expertise in tools like SQL, Python, R, Tableau, and Power BI.
- Industry & Location Impact:Earnings differ across sectors like finance, healthcare, and tech, as well as by geographic region.
Data Engineer
Roles and Responsibility for Data Analyst
Roles and Responsibility for Data Analyst
Data Collection
Gather data from various sources such as databases, surveys, APIs, and online platforms to build a comprehensive dataset for analysis.
Data Cleaning
Address issues like duplicates, missing values, and inaccuracies by cleaning and transforming the data into a consistent, reliable format.
Data Analysis
Apply statistical techniques and tools to uncover patterns, trends, and meaningful insights that can drive business decisions.
Data Visualization
Create visual representations such as graphs, charts, and dashboards to make findings easily understandable and actionable for stakeholders.
Reporting
Present findings through detailed reports or presentations, highlighting key insights and offering recommendations to support business strategies.
Building Reports and Dashboards
Design and automate reports and dashboards for continuous data tracking, allowing real-time monitoring of key metrics and performance indicators.
Collaboration
Collaborate with cross-functional teams (such as marketing, product, and operations) to provide data-driven recommendations and support informed decision-making.
Data Interpretation
Translate complex data into clear, actionable insights that guide strategic decisions and improve business outcomes.
Course Duration
Data Analyst in 4 Months?
Can I Learn Data Analysis in 4 Months?
Yes, it’s absolutely possible to learn Data Analysis in 4 months with strong focus and commitment. By dedicating 3-4 hours daily to studying, practicing, and working on hands-on projects, you can develop the necessary skills. Completing real-world projects will not only reinforce your learning but also help you build a portfolio that will impress potential employers.
With consistency, a clear study plan, and a focus on practical application, you can be job-ready by the end of 4 months.