Best Data Analyst Course In Pune With Placement
100% Placement Assistance | Live Online Sessions
3 Month Personalized Live Data Analyst Training is taught by industry experts in a comprehensive & question-oriented format.
Enroll Before: 07 December, 2024
2000+
Students Trained
100%
Placement Assistance
07 December, 2024
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 Analyst 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
Introduction to Data Analysis
Data Analysis is the process of examining, cleaning, and modeling data to uncover useful insights that support decision-making. It’s like a detective using data to find patterns and trends that help businesses and organizations make informed choices. As data grows, data analysis becomes essential across industries to understand and leverage this information.
Why Data Analysis Matters
Data analysis is crucial because data is everywhere: from online purchases to social media interactions. By analyzing this data, businesses can:
- Understand customer preferences
- Improve products and services
- Predict trends and behaviors
Data analysts play a key role in interpreting data and supporting data-driven decisions.
Key Components of Data Analysis
Data Collection
Gathering data from various sources like databases, surveys, and websites.Data Cleaning
Identifying and fixing errors, duplicates, and missing values in the raw data.Data Analysis
Using statistical methods and tools (Excel, SQL, Python, or R) to identify patterns and insights.
The Data Analysis Process
- Collect data
- Clean and prepare
- Analyze
- Create visualizations and reports
- Present findings
Each stage builds upon the last to deliver actionable insights.
How Data Analysis Projects Are Structured
- Define the problem or goal (e.g., sales analysis)
- Collect and clean relevant data
- Analyze data for trends and insights
- Create visualizations (charts, graphs)
- Present findings to stakeholders
The Role of a Data Analyst
Data analysts analyze data to help businesses make informed decisions. They use skills in statistics, programming, and visualization to extract meaningful insights.
Key Responsibilities of Data Analysts
- Collect and clean data
- Analyze and interpret trends
- Build reports and dashboards
- Create visualizations for communication
- Collaborate with teams for data-driven decisions
Applications of Data Analysis
- Healthcare: Predicting disease outbreaks and improving treatments.
- E-commerce: Enhancing product recommendations and pricing strategies.
- Finance: Detecting fraud and managing risks.
- Marketing: Optimizing campaigns and targeting customers.
How to Get Started in Data Analysis
Courses for Beginners
Start with online courses covering statistics, Excel, SQL, data cleaning, and visualization.Practical Experience
Work on real datasets (e.g., Kaggle) to practice and build a portfolio, showcasing your skills to employers.
Eligibility
for Data Analyst Course​
Any Graduate Background
You don’t need a specific background. Whether you studied engineering, business, science, or arts, the course is open to all graduates. A solid educational foundation and willingness to learn are key.Interest in Coding
No prior coding experience required, but an interest in learning programming (Python, R, SQL) will be helpful. The course will guide you step-by-step from the basics.Time Commitment
Plan to dedicate 3-4 hours per day for lectures, assignments, and practice. Consistency is key to mastering the skills needed for data analysis.No Prior Experience Needed
The course is designed for beginners. You’ll start with fundamentals and gradually progress to more advanced topics.Curiosity & Problem-Solving Mindset
If you’re curious, enjoy solving problems, and think analytically, data analysis is a great fit for you. A problem-solving mindset will help you thrive in this field.
FAQs
Frequently asked questions
1.Do I need any prior experience to enroll in a Data Analyst course?
No, prior experience is not required. The course is designed for beginners, and you’ll start with the basics, gradually advancing to more complex topics.
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2.What topics are covered in a Data Analyst course?
Typical topics include data cleaning, SQL, data visualization (using tools like Tableau or Power BI), statistics, Excel, Python or R for analysis, and creating reports and dashboards.
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3.How long does it take to complete a Data Analyst course?
The duration varies, but most courses can be completed in 3-6 months, depending on whether you’re studying part-time or full-time.
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4.Do I need to learn coding for Data Analysis?
Yes, you’ll need to learn basic programming for data manipulation and analysis. Python and SQL are the most commonly taught languages for Data Analysts.
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5.Can I take a Data Analyst course online?
Yes, there are many online platforms offering Data Analyst courses, such as Coursera, edX, Udemy, and DataCamp, with flexible schedules.
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6.What tools will I learn to use during the course?
You will learn tools like Excel, SQL, Python (or R), Tableau, Power BI, and data manipulation libraries like Pandas and NumPy.
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7.Will I get hands-on experience in a Data Analyst course?
Yes, most courses offer hands-on projects and assignments to help you apply what you learn. Real-world datasets and case studies are used to build practical skills.
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8.What career opportunities are available after completing a Data Analyst course?
After completing a course, you can apply for roles such as Data Analyst, Business Intelligence Analyst, or Data Consultant across industries like finance, healthcare, e-commerce, and marketing.
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9.How much can I expect to earn after completing a Data Analyst course?
Salaries vary by location, experience, and industry, but the average salary for a Data Analyst typically ranges from $50,000 to $80,000 annually.
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10.Will I receive a certificate after completing the course?
Most courses offer a certificate of completion, which can be useful for job applications or building your professional profile on platforms like LinkedIn.
Data Engineer
Roles and Responsibility for Data Analyst
Roles and Responsibility for Data Analyst
Data Collection
Gather data from various sources like databases, surveys, and APIs.Data Cleaning
Clean and preprocess raw data by removing duplicates, handling missing values, and correcting errors.Data Analysis
Analyze data to identify trends, patterns, and insights using statistical methods and tools.Data Visualization
Create visualizations (charts, graphs, dashboards) to present findings clearly and effectively.Reporting
Communicate insights and recommendations to stakeholders through reports or presentations.Building Reports and Dashboards
Develop and maintain automated reports and dashboards for ongoing analysis.Collaboration
Work closely with other teams (e.g., marketing, product) to support data-driven decision-making.Data Interpretation
Translate complex data into actionable insights for business strategy and operations.
Course Duration
Data Analyst in 3 Months?​
Can I Learn Data Analysis in 3 Months?
Yes, it’s definitely possible to learn Data Analysis in 3 months, but it requires strong commitment and focus. To succeed, you’ll need to dedicate 3-4 hours per day for consistent study and practice. This time should be spent not just watching tutorials but also solving real-world problems, doing hands-on exercises, and applying concepts like data cleaning, analysis, and visualization.
In addition to theory, working on projects is essential. Building a portfolio of real-world projects will not only reinforce your learning but also make you stand out to potential employers. The more projects you complete, the more confident you’ll be in your abilities.
While a clear study plan and consistency are crucial, staying focused and avoiding distractions will help you cover all the key topics in Data Analysis. By the end of 3 months, with dedication and the right approach, you’ll be well-prepared to start applying for jobs.
In short: With consistent effort and a practical approach, you can become job-ready in 3 months.