Best Data Analyst Course In Mumbai 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 raw data to uncover insights that inform decision-making. Analysts identify patterns, trends, and correlations to help organizations make data-driven choices. As data becomes more abundant, the ability to analyze it effectively is increasingly important.
Why Data Analysis Matters
Data is everywhere, from online purchases to social media activity. By analyzing it, businesses can:
- Understand customer behavior: Personalize services and products.
- Optimize operations: Improve efficiency and streamline processes.
- Predict trends: Forecast market shifts and customer actions.
Data analysts transform this raw data into actionable insights that drive smart business decisions.
Key Components of Data Analysis
- Data Collection: Gather data from internal and external sources like databases, surveys, and APIs.
- Data Cleaning: Remove errors, inconsistencies, and missing values to ensure reliable analysis.
- Data Analysis: Use statistical tools (Excel, SQL, Python, R) to identify patterns and trends.
The Data Analysis Process
- Collect Data: Gather data from multiple sources.
- Clean and Prepare: Remove outliers and handle missing data.
- Analyze: Apply statistical techniques to find trends and correlations.
- Create Visualizations: Present insights using charts and graphs.
- Interpret and Present: Share results with stakeholders for decision-making.
How Data Analysis Projects Are Structured
- Define the Problem: Identify the business goal (e.g., improving sales or customer satisfaction).
- Collect and Clean Data: Gather and prepare relevant data.
- Conduct Analysis: Analyze the data for actionable insights.
- Create Visualizations: Use visuals to communicate findings.
- Report Findings: Present insights clearly to stakeholders.
The Role of a Data Analyst
Data analysts transform raw data into actionable insights to guide business decisions. They use tools like Excel, SQL, Python, R, and data visualization software (e.g., Tableau or Power BI).
Key Responsibilities of Data Analysts
- Collect and clean data
- Analyze trends and patterns
- Create reports and dashboards
- Collaborate with stakeholders to solve problems
Applications of Data Analysis
- Healthcare: Improve patient care and predict disease outbreaks.
- E-commerce: Optimize product recommendations and pricing.
- Finance: Detect fraud and assess risks.
- Marketing: Analyze customer behavior to optimize campaigns.
How to Get Started in Data Analysis
- Take Courses: Start with beginner courses on platforms like Coursera, Udemy, or edX.
- Gain Practical Experience: Work on real-world datasets (e.g., Kaggle, data.gov) to build a portfolio.
- Network: Join data analysis communities, seek mentorship, or look for internships to gain hands-on experience.
Eligibility
for Data Analyst Course​
FAQs
Frequently asked questions
1.What is a Data Analyst?
A data analyst is a professional who collects, processes, and analyzes data to uncover trends, patterns, and insights. They use various tools to help businesses and organizations make data-driven decisions.
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2.Do I need a specific educational background to become a data analyst?
No, you don’t need a specific degree. Whether you have a background in engineering, business, arts, or any other field, you can become a data analyst as long as you have a solid educational foundation and a willingness to learn.
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3.What skills are needed to become a data analyst?
Key skills for data analysts include:
- Proficiency in Excel and SQL
- Basic knowledge of programming languages like Python or R
- Strong analytical and problem-solving abilities
- Data visualization skills (e.g., using tools like Tableau or Power BI)
- Good communication skills to present findings clearly.
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4.How long does it take to become a data analyst?
It typically takes around 3-6 months to learn the basics of data analysis if you’re consistent in practicing and learning. This timeline may vary depending on how much time you dedicate each day.
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5.Do I need coding experience to become a data analyst?
No, prior coding experience is not required. Basic programming knowledge in languages like Python or SQL will be taught during the course, and you’ll learn progressively from the basics.
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6.What tools do data analysts use? Data analysts use tools like:
- Excel for data manipulation and analysis
- SQL for querying databases
- Python or R for advanced data analysis and statistical tasks
- Tableau, Power BI, or Google Data Studio for data visualization.
7.What industries need data analysts? Data analysts are in demand across various industries, including:
- Healthcare (analyzing patient data, improving treatment plans)
- Finance (fraud detection, investment analysis)
- E-commerce (customer behavior analysis, product recommendations)
- Marketing (campaign effectiveness, customer segmentation).
8.Can I get a job as a data analyst without a degree in data science? Yes, it’s possible to get a data analyst job without a formal degree in data science. Many employers look for practical skills and a strong portfolio of projects rather than just a degree.
9.What kind of projects should I work on as a beginner? As a beginner, you should work on projects like:
- Sales analysis or customer behavior analysis using publicly available datasets (e.g., Kaggle, UCI Machine Learning Repository).
- Data cleaning and visualization tasks to hone your skills.
- Building dashboards to present findings clearly.
10.How much can a data analyst earn?
Data analyst salaries can vary based on location, experience, and skills. On average, in the U.S., data analysts earn between $50,000 to $80,000 per year, with more experienced analysts earning higher salaries. However, salaries may vary significantly in different countries and industries.
Data Engineer
Roles and Responsibility for Data Analyst
Roles and Responsibility for Data Analyst
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 with strong focus and commitment. Dedicate 3-4 hours daily to study, practice, and work on hands-on projects. Completing real-world projects will not only reinforce your skills but also build a portfolio to impress employers. With consistency, a clear study plan, and practical application, you’ll be ready to apply for jobs by the end of 3 months.
In short: With focused effort and practical experience, you can become job-ready in 3 months.