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Best Data Analytics Course In Bangalore 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: 07 December, 2024

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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 )

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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

Get familiar with our online Data Anaylst course syllabus.

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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.

Data analyst course in banglore

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 Visuals 
  • Advanced DAX             
  • Power Query M Language
  • Power BI Service              
  • Data Refresh and Gateways    
  • Power BI Administration     
  • Dataflow                  
  • Paginated Reports             
  • Performance Optimization      
  • Power BI Embedded         
  • Final Project and Review 

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

0 +
HOURS OF LIVE LEARNING
0 +
Live Projects
0 +
HOURS OF VIDEO LEARNING

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Detailed Course Syllabus & Trainer List

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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.

Get More about this 

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Tools You’ll Master

Mentors

Shamli-removebg-preview
Shamli

Python Trainer
4+ Years ITExperience
4+ Years Teaching Experience

Govind Chavan
Govind Chavan

Project Trainer
5+ Years IT Experience
5+Teaching Experience

Rushikesh Deshmukh
Rushikesh Deshmukh

Digital Marketing
3+ Years IT Experience
2+Teaching Experience

Bharat
Bharat Kale

Manual Trainer
5+ Years IT Experience
5+Teaching Experience

Our Learners Work At

Enroll Now and get 5% Off On Course Fees

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Introduction to Data Analytics
Data Analytics is the process of examining and interpreting raw data to uncover valuable insights that inform decision-making. Analysts use various techniques to identify patterns, trends, and correlations in the data, helping organizations make informed, data-driven decisions. As the amount of data continues to grow, effective analysis is becoming increasingly important.

Why Data Analytics Matters
Data is generated everywhere, from online purchases to social media activity. By analyzing this data, businesses can:

  • Understand customer behavior: Personalize services and products to meet customer needs.
  • Optimize operations: Improve efficiency, reduce costs, and streamline processes.
  • Predict trends: Anticipate market shifts, consumer actions, and potential business challenges.

Data analysts transform raw data into actionable insights that drive smart, informed business decisions.

Key Components of Data Analytics

  • Data Collection: Gather data from internal and external sources such as databases, surveys, APIs, and sensors.
  • Data Cleaning: Remove errors, inconsistencies, duplicates, and handle missing values to ensure that the data is reliable and accurate.
  • Data Analysis: Use statistical and analytical tools (e.g., Excel, SQL, Python, R) to explore and identify meaningful patterns, trends, and correlations.

The Data Analytics Process

  1. Collect Data: Gather data from multiple, relevant sources.
  2. Clean and Prepare: Clean the data by addressing missing values, outliers, and formatting issues.
  3. Analyze: Apply statistical techniques or machine learning algorithms to uncover patterns, trends, and correlations in the data.
  4. Create Visualizations: Use charts, graphs, and other visual aids to make the findings easier to interpret.
  5. Interpret and Present: Communicate the insights and recommendations to stakeholders for informed decision-making.

How Data Analytics Projects Are Structured

  1. Define the Problem: Clearly identify the business problem or goal (e.g., increasing customer retention or improving operational efficiency).
  2. Collect and Clean Data: Gather and prepare data relevant to the problem.
  3. Conduct Analysis: Analyze the data to identify patterns, trends, and actionable insights.
  4. Create Visualizations: Design clear and informative visuals to present the data findings.
  5. Report Findings: Share the insights with stakeholders through reports and presentations to guide decision-making.

The Role of a Data Analyst
Data analysts transform raw data into actionable insights that inform strategic decisions. They use a variety of tools such as Excel, SQL, Python, R, and data visualization platforms (e.g., Tableau, Power BI).

Key Responsibilities of Data Analysts

  • Collect and clean data to ensure quality and accuracy.
  • Analyze trends and patterns to derive meaningful insights.
  • Create reports, dashboards, and visualizations for stakeholders.
  • Collaborate with business teams to solve problems and improve decision-making processes.

Applications of Data Analytics

  • Healthcare: Enhance patient care, predict disease outbreaks, and optimize healthcare resource management.
  • E-commerce: Personalize product recommendations, improve pricing strategies, and optimize customer experience.
  • Finance: Detect fraud, assess investment risks, and optimize portfolio management.
  • Marketing: Analyze customer behavior, optimize marketing campaigns, and improve customer segmentation.

How to Get Started in Data Analytics

  1. Take Courses: Start with beginner-level courses on platforms like Coursera, Udemy, or edX to build foundational knowledge.
  2. Gain Practical Experience: Work on real-world datasets from platforms like Kaggle or data.gov to apply your skills and build a portfolio.
  3. Network: Join data analytics communities, participate in meetups or forums, and seek mentorship to gain more exposure to the field.

Data analytics is an essential skill for businesses and individuals looking to turn raw data into actionable insights that drive strategic decisions.

data science course in pune

Eligibility

for Data Analyst Course​

  1. Graduate Background: Open to all graduates, no specific field required.
  2. Interest in Coding: No prior experience needed; willingness to learn Python, R, and SQL.
  3. Time Commitment: 3-4 hours per day for lectures and practice.
  4. No Prior Experience: Designed for beginners, starting with the fundamentals.
  5. Curiosity & Problem-Solving: A curious and analytical mindset will help you succeed.

FAQs

Frequently asked questions

  1. What is a Data Analytics Course?
    A Data Analytics course teaches individuals how to collect, process, and analyze data to uncover insights. It covers essential skills like using tools for data analysis, identifying trends, and visualizing data to support decision-making in business.

  2. Do I need a specific educational background to take a Data Analytics course?
    No, you don’t need a specific degree to take a Data Analytics course. The course is open to individuals from any academic background, as long as you have a basic understanding of math and a willingness to learn.

  3. What skills are taught in a Data Analytics course?
    Key skills in a Data Analytics course include:

    • Proficiency in Excel and SQL for data manipulation and querying
    • Introduction to programming languages like Python or R for advanced analytics
    • Data visualization techniques using tools like Tableau or Power BI
    • Analytical and problem-solving skills for interpreting data
    • Communication skills to present insights clearly and effectively.

  4. How long does it take to complete a Data Analytics course?
    Most Data Analytics courses take 3-6 months to complete, depending on your commitment and time availability. Some accelerated programs may offer faster tracks.

  5. Do I need coding experience before starting a Data Analytics course?
    No prior coding experience is required. Basic programming skills in languages like Python and SQL will be taught as part of the course, starting from the fundamentals.

  6. What tools will I learn in a Data Analytics course?
    A Data Analytics course typically includes training on tools such as:

    • Excel for data analysis and manipulation
    • SQL for querying and managing databases
    • Python or R for performing advanced data analysis and statistical operations
    • Tableau, Power BI, or Google Data Studio for creating data visualizations and dashboards.

  1. What industries need data analysts?
    Data analysts are in demand across various industries, including:

    • Healthcare: Analyzing patient data to improve treatment plans and healthcare outcomes.
    • Finance: Fraud detection, investment analysis, and risk management.
    • E-commerce: Analyzing customer behavior, optimizing product recommendations, and enhancing customer experience.
    • Marketing: Analyzing campaign effectiveness, customer segmentation, and market trends.

  2. Can I get a job as a data analyst without a degree in data science?
    Yes, it’s possible to land a data analyst job without a formal data science degree. Many employers prioritize practical skills, hands-on experience, and a strong project portfolio over a specific degree in data science.

  3. What kind of projects should I work on as a beginner?
    As a beginner, focus on projects that build foundational skills, such as:

    • Sales analysis or customer behavior analysis using publicly available datasets (e.g., Kaggle, UCI Machine Learning Repository).
    • Data cleaning and visualization tasks to practice data preprocessing and presentation.
    • Building dashboards to showcase your ability to present data insights clearly.

  4. How much can a data analyst earn?
    Data analyst salaries vary based on factors like location, experience, and skills. In the U.S., salaries typically range from $50,000 to $80,000 per year. Experienced analysts and those with specialized skills can earn more. Salaries may differ across countries and industries.

Data Engineer

Roles and Responsibility for Data Analyst

Roles and Responsibility for Data Analyst

1.Data Collection
Gather data from different sources like databases, surveys, and APIs.

2.Data Cleaning
Fix issues in the data, like removing duplicates, filling missing values, and correcting errors.

3.Data Analysis
Use tools and methods to find patterns, trends, and insights in the data.

4.Data Visualization
Create graphs, charts, and dashboards to clearly show the results.

5.Reporting
Share findings and suggestions with stakeholders through reports or presentations.

6.Building Reports and Dashboards
Set up automated reports and dashboards for ongoing data monitoring.

7.Collaboration
Work with different teams (like marketing or product) to help make decisions based on data.

8.Data Interpretation
Turn complex data into clear, actionable insights that help guide business strategies.

 
 

Course Duration

Data Analyst in 4 Months?

Can I Learn Data Analysis in 4 Months?

Yes, it’s definitely possible to learn Data Analysis in 4 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 4 months.

In short: With focused effort and practical experience, you can become job-ready in 4 months.

Enroll Now and get 5% Off On Course Fees

Enroll Now and get 5% Off On Course Fees

Enroll Now and get 5% Off On Course Fees

Enroll Now and get 5% Off On Course Fees