Best Data Analytics Course In Ahmednagar 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
0%
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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Data Analytics Course in Ahmednagar
Introduction to Data Analytics
Data Analytics is the process of analyzing raw data to uncover valuable insights that drive smarter decision-making. By identifying patterns, trends, and correlations, businesses can enhance efficiency, streamline operations, and develop data-driven strategies.
Why Data Analytics Matters?
- Understand Customer Behavior: Personalize products and services.
- Optimize Operations :Â Improve efficiency and reduce costs.
- Predict Trends:Â Anticipate market changes and customer 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 and reliable information from various sources.
- Clean & Prepare Data –Ensure accuracy, consistency, and completeness.
- Analyze Data – Apply statistical methods and machine learning techniques.
- Create Visualizations –Represent insights through interactive charts and graphs.
- Interpret & Present Insights – Effectively communicate findings for informed decision-making.
Role of a Data Analyst
A Data Analyst transforms raw data into actionable insights using tools like Excel, SQL, Python, Tableau, and Power BI. Their expertise helps businesses make data-driven decisions, improve efficiency, and drive growth.
Applications of Data Analytics
- Healthcare:Â Improve patient care, optimize resource allocation, and streamline operations.
- E-commerce: Personalize user experiences, refine pricing strategies, and enhance product recommendations.
- Finance: Â Detect fraud, assess investment risks, and improve financial decision-making.
- Marketing: Optimize campaigns, refine customer segmentation, and boost engagement through data-driven strategies.
How to Get Started
- Enroll in Courses –Learn from structured programs on platforms like Coursera, Udemy, and edX.
- Gain Practical Experience –Work on real-world datasets through internships, projects, and Kaggle competitions.
- Network & Grow – Join data analytics communities fr mentorship and career guidance.
Eligibility
for Data Analyst Course​
Eligibility & Program Requirements
- Open to All Graduates – No specific background required. Any degree holder 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?
Finding the right data analytics course can fast-track your learning and career growth. Keep these key factors in mind:
- Comprehensive Curriculum –  Covers everything from fundamentals to advanced techniques.
- Hands-on Learning – Includes real-world projects, case studies, and practical exercises.
- Expert Instructors –Learn from industry professionals with real-world experience.
- Career Support –  Look for job placement assistance, resume-building guidance, and industry-recognized certifications.
2. Can I Teach Myself Data Analytics?
Yes! You can absolutely learn data analytics on your own through free online resources, tutorials, and community forums. However, structured courses offer a more organized learning path, hands-on experience, and faster skill development.
3. What Are the Most Essential Tools for Data Analytics?
Data analysts rely on various tools to process, analyze, and visualize data, including:
- 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 converts complex information into clear, engaging visuals, making it easier to analyze, interpret, and act on data-driven insights. By using charts, graphs, and interactive dashboards, businesses can quickly identify patterns, trends, and correlations.
Modern tools like Tableau and Power BI enable users to create dynamic reports, making data-driven storytelling more effective and impactful.
5. Is Data Analytics a Good Career Choice?
Absolutely! Data Analytics is one of the fastest-growing and most in-demand career fields today. With industries like healthcare, finance, e-commerce, and marketing increasingly relying on data-driven decisions, professionals in this field enjoy strong job prospects, competitive salaries, and career growth opportunities.
6. How Is Data Analytics Used in Real Life?
- Marketing: Personalizing campaigns based on customer behavior.
- Finance: Detecting fraud and assessing risks.
- Retail: Predicting inventory needs and optimizing supply chains.
- Healthcare:Enhancing patient care with 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: Salaries differ across sectors such as 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.