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Best Data Analytics Course In Sambhaji Nagar 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: 04th January, 2025

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Students Trained

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Placement Assistance

04 January, 2025

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7:30 AM - 9:30 AM

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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 Analytics course in Sambhaji Nagar

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

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Data Analytics Course in Sambhaji Nagar

Introduction to Data Analytics

Data Analytics is the process of examining raw data to uncover valuable insights that drive decision-making. Analysts utilize various techniques to identify patterns, trends, and correlations, helping organizations make data-driven decisions. As data volumes continue to grow, mastering the art of data analysis is becoming more essential than ever.

Why Data Analytics Matters

Data is generated at every touchpoint—whether from online transactions, social media interactions, or customer feedback. By analyzing this data, businesses can:

  • Understand Customer Behavior: Tailor products and services to meet customer preferences.
  • Optimize Operations: Enhance efficiency, cut costs, and streamline processes.
  • Predict Trends: Anticipate market shifts, forecast consumer behavior, and identify potential challenges.

Data analysts turn raw data into actionable insights, enabling organizations to make smarter, more informed decisions.

Key Components of Data Analytics

  1. Data Collection: The first step is gathering relevant data from diverse sources such as databases, APIs, surveys, or sensors.
  2. Data Cleaning: Ensuring data accuracy by handling missing values, eliminating duplicates, and fixing inconsistencies.
  3. Data Analysis: Using tools like Excel, SQL, Python, and R to explore the data, identify trends, and draw meaningful conclusions.

The Data Analytics Process

  1. Collect Data: Gather data from internal and external sources.
  2. Clean and Prepare: Cleanse the data by addressing missing values, outliers, and formatting issues to ensure reliability.
  3. Analyze: Apply statistical methods or machine learning techniques to uncover patterns and relationships within the data.
  4. Create Visualizations: Use charts, graphs, and other visual aids to simplify complex findings and make them easier to understand.
  5. Interpret and Present: Share insights and recommendations with stakeholders to aid in informed decision-making.

How Data Analytics Projects Are Structured

  1. Define the Problem: Clearly identify the business challenge, such as increasing customer retention or improving operational efficiency.
  2. Collect and Clean Data: Gather and prepare the necessary data.
  3. Conduct Analysis: Analyze the data to uncover actionable insights.
  4. Create Visualizations: Design intuitive visuals to present your findings.
  5. Report Findings: Share your insights with stakeholders through presentations or reports to guide business decisions.

The Role of a Data Analyst

A data analyst’s job is to transform raw data into meaningful insights. By using tools like Excel, SQL, Python, R, and data visualization platforms (e.g., Tableau, Power BI), data analysts empower businesses to make better strategic decisions.

Key Responsibilities of Data Analysts

  • Collect and Clean Data: Ensure the data is accurate, complete, and ready for analysis.
  • Analyze Trends and Patterns: Identify key trends and actionable insights from the data.
  • Create Reports and Dashboards: Design visual representations of data to communicate findings effectively.
  • Collaborate with Teams: Work closely with business teams to solve problems and improve decision-making.

Applications of Data Analytics

  • Healthcare: Improve patient care, predict outbreaks, and optimize resource allocation.
  • E-commerce: Personalize recommendations, optimize pricing, and enhance customer experiences.
  • Finance: Detect fraud, manage investment risks, and optimize portfolio strategies.
  • Marketing: Analyze customer behavior, refine marketing campaigns, and optimize segmentation.

How to Get Started in Data Analytics

  1. Take Courses: Start with beginner courses on platforms like Coursera, Udemy, or edX to build foundational skills.
  2. Gain Practical Experience: Apply your knowledge to real-world datasets from sites like Kaggle or data.gov to build a portfolio.
  3. Network: Join online data analytics communities, attend meetups, and seek mentorship to broaden your knowledge and career prospects.

Data Analytics is a crucial skill for those looking to harness the power of data. Whether you’re a business professional or an individual looking to enhance your decision-making abilities, data analytics can help transform raw data into meaningful insights that drive success.

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Eligibility

for Data Analyst Course​

Eligibility and Requirements for the Program

  • Graduate Background: Open to all graduates; no specific field of study required.
  • Interest in Coding: No prior coding experience needed. A willingness to learn programming languages like Python, R, and SQL is essential.
  • Time Commitment: Dedicate 3-4 hours per day for lectures, hands-on practice, and learning.
  • No Prior Experience: This program is designed for beginners, starting from the very basics.
  • Curiosity & Problem-Solving: A curious mind and an analytical approach will set you up for success.

FAQs

Frequently asked questions

 

What is a Data Analytics Course?

A Data Analytics course teaches individuals how to collect, process, and analyze data to uncover valuable insights. The course covers essential skills such as using analytical tools, identifying trends, and visualizing data to support decision-making processes in businesses.

Do I need a specific educational background to take a Data Analytics course?

No, you don’t need a specific degree to enroll in a Data Analytics course. It is open to individuals from any academic background, as long as you have a basic understanding of math and a strong willingness to learn.

What skills are taught in a Data Analytics course?

Key skills taught 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 to interpret data effectively
  • Communication skills to present insights clearly and effectively to stakeholders

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 available time. Some accelerated programs offer faster tracks for those who wish to complete the course more quickly.

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 and progressing to more advanced techniques.

What tools will I learn in a Data Analytics course?

A Data Analytics course typically includes training on the following tools:

  • Excel for basic 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

What types of industries can benefit from Data Analytics skills?

Data Analytics skills are applicable across various industries, including:

  • Healthcare: Improving patient care, predicting disease trends, and optimizing resources
  • Finance: Risk assessment, fraud detection, and portfolio optimization
  • E-commerce: Personalizing customer experiences and optimizing inventory
  • Marketing: Customer segmentation, campaign optimization, and consumer behavior analysis
  • Manufacturing: Improving operational efficiency and predicting equipment failures

Is a Data Analytics course suitable for beginners?

Yes, Data Analytics courses are designed for beginners. They start with foundational concepts and gradually introduce more advanced topics. No prior experience is needed, making it a great option for those new to the field.

Will a Data Analytics course help me get a job?

While completing a Data Analytics course provides valuable skills, getting a job depends on several factors, including your practical experience, portfolio, and networking. However, many courses offer career support, such as resume-building workshops, interview coaching, and job placement assistance, to increase your chances of securing a position.

How can I apply the knowledge gained in a Data Analytics course in real life?

The knowledge gained in a Data Analytics course can be applied in real-world scenarios, such as:

  • Analyzing business performance to identify areas of improvement
  • Creating dashboards for tracking KPIs and business metrics
  • Identifying trends in customer behavior to inform marketing strategies
  • Forecasting sales and inventory requirements based on historical data

What are the prerequisites for a Data Analytics course?

Typically, the prerequisites for a Data Analytics course include:

  • A basic understanding of mathematics, particularly statistics
  • Familiarity with Microsoft Excel for data manipulation
  • A willingness to learn programming languages like Python or R (though these will be taught during the course)

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 -5 Months?

Can I Learn Data Analysis in 4-5 Months?

Yes, it’s absolutely possible to learn Data Analysis in 4-5 months with strong focus and commitment. By dedicating 3-4 hours daily to studying, practicing, and working on hands-on projects, you can gain 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 well-organized study plan, and a focus on practical application, you can be job-ready by the end of 4-5 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