|
Ready to deep dive into the world of Data Analytics to become excel in Data Analytics? So, get ready to resolve all the doubts of your curious minds. Yes, you hear right. We GeeksforGeeks with our comprehensive ‘Data Analytics Training‘ using Excel, SQL, Python & PowerBI help you get deep insights about mastering data analytics for data analysis, visualization, and reporting. To help organizations make big data-driven organization decisions, identify patterns & trends, and extract large amounts of informational data to bring up meaningful data insights, and analytics plays a huge role.
In fact, “The Data Analytics market size is projected to grow from $ 7.03 billion in 2023 to $ 303.4 billion by 2030 at a CAGR of 27.6%”, indicating the growing demand for data analysts due to the increased reliance on data-driven decision-making across various industries.
But from where to start? Don’t worry, to help you gain invaluable insights of data analytics using different tools and languages like Excel, SQL, Python & PowerBI, we with our ‘Data Analytics Training Course’ will let you master the valuable skills and techniques of Data Analysis. So, Here you go learners!
Welcome to our ‘Data Analytics Training Course‘! The GeeksforGeeks ‘Data Analytics Training using Excel, SQL, Python & PowerBI‘ is a completely comprehensive course designed to assist you in gaining proficiency in Python, SQL, Excel & Tableau for data analysis, visualization & reporting processes. The course helps you master in-depth learning of the data analytics process with an understanding of the working of OS using Python, Excel, SQL, PowerBI, Jupyter, Pandas & other data- preprocessing techniques for better data management and analysis.
This 11-week, Beginner to Advance Live Course is designed ultimately to explore your learning potential, preparing you to excel in Data Analyst roles.
Why Choose Our Course:
- Master essential analytical tools like Python, Pandas, Jupyter, Numpy, Excel, SQL, Tableau & more.
- Hands-on-experience with Real-world datasets.
- Deep dive into various exquisite practical projects like:
- E-Commerce Product Analysis
- Movie Industry Analysis
- Food Industry Analysis
- 100K Book Analysis
- Avail 90% Fee Refund on the course
- Learn Advanced Excel formulas and functions for data management
- Master Statistical Analysis for visualizations and interactivity.
Don’t miss this opportunity! Join the course now! Become a data analyst expert and start your career journey in one of the fastest-growing field of data analytics and dare to stay a part of the competition.
Key Highlights:
- 2 Live Classes Every Week
- 30+ hours of Beginner to Advanced Self-Paced Content
- Work on Multiple Real-Life Projects and implementation
- Industry Recognized Certificate
- Articles to supplement the learning experience
- Learn industrial tools including Pandas, Jupyter, Numpy, Excel, SQL, Tableau, and more
- Hands-on practice with Real-World Datasets
- Career Guidance: Receive guidance on career opportunities and the next steps in your Data Analytics journey as a Data Analyst, Business Analyst, Quality Assurance Analyst, or Market Research Analyst
- Avail the benefit of 90% fee refund upon completing 90% course in 90 days.
Syllabus:
Week 1: Excel
Day 1: Introduction to Excel for Data Analysis
- Overview of Excel interface
- Basics of navigating and working with sheets
- Introduction to cells, rows, columns, and ranges
- Understanding basic functions (SUM, AVERAGE, COUNT)
- Working with mathematical and statistical functions
- Introduction to text functions for data manipulation
Day 2: Advanced Formulas and Functions
- Working with logical functions (IF, AND, OR)
- Exploring lookup functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
- Introduction to array formulas
- Identifying and handling missing data
- Removing duplicates and dealing with errors
- Text-to-columns and data-splitting techniques
- Formatting data for analysis
- Creating basic charts and graphs
- Tips for effective data presentation
- Introduction to PivotTables for dynamic data analysis
- Creating Pivot Charts for visual insights
- Customizing and formatting PivotTables and Pivot Charts
- Time-saving shortcuts and productivity hacks
- Excel with AI
Week 2: SQL
Day 1: Introduction to SǪL and Database Fundamentals
- Overview of SǪL and its applications
- Introduction to Relational Databases
- Basic SǪL syntax and structure
- Creating and modifying tables with CREATE and ALTER
- Understanding data types and constraints
Day 2: Retrieving Data with SELECT Statements
- Basics of SELECT statements
- Filtering data with WHERE clause
- Sorting results with ORDER BY
Week 3: Advanced SǪL Techniques
Day 1: Aggregation and Grouping
- Understanding aggregate functions (SUM, AVG, COUNT)
- Grouping data with GROUP BY
- Working with complex WHERE conditions
- Using operators (AND, OR, NOT, etc)
Day 2: Window Functions and Analytic Queries
- Introduction to window functions
- Performing analytic queries with OVER clause
Week 4: Advance SǪL
Day 1: Joins and Subqueries
- Performing INNER and OUTER joins
- Using subqueries for complex queries
Day 2: Case Statements and CTE Queries
- Understanding and using CASE statements in SǪL
- Applying CASE statements in data analysis scenarios
- Introduction to Common Table Expressions
- Using CTEs for recursive queries and data manipulation
Week 5: More on SQL
Day 1: Time-saving shortcuts and productivity hack
- Optimization of queries
- Optimization of queries using AI
- Interview based SǪL queries
Day 2: Working on live project
- Working on industry orient data
- Problem-solving using SǪL on industrial data
Week 6: Introduction to Python for Data Analysis
Day 1: Introduction to Python and Jupyter Notebooks
- Overview of Python programming language
- Introduction to Jupyter Notebooks for data analysis
- Variables, data types, and basic operations
- Lists, tuples, and dictionaries
- Inbuilt functions
Day 2: Data Manipulation with Python
- Conditional statements and loops
- User defined functions
- Functions such as map, filter, lambda
Week 7: Exploring Data with Pandas & Matplotlib
Day 1: Data Manipulation with Pandas
- Overview of Pandas Library
- Reading and writing data along with basic operations with Pandas
Day 2: Data Cleaning and Preprocessing with Pandas
- Handling missing data
- Removing duplicates and dealing with outliers
- Cleaning and adjustments in data
Week 8: EDA & Data Visualization
Day 1: Exploratory Data Analysis (EDA) with Pandas
- Descriptive statistics and data summarization
- Grouping and aggregating data
- SǪL like operation in data
Day 2: Data Visualization with Matplotlib
- Creating basic plots (line plots, scatter plots, histograms)
- Customizing and styling visualizations
Week 9: Real-time Python
Day 1: Advanced Data Analysis with Numpy
- Introduction to Numpy for numerical operations
- Working with arrays and matrices
Day 2: Advanced Data Visualization with Seaborn
- Creating informative and aesthetically pleasing visualizations
- Pair plots, heatmaps, and advanced plotting technique
Week 10: Statistical Analysis
Day 1: Data Modeling and Relationships in Power BI
- Creating a data model in Power BI
- Understanding relationships between tables
- Implementing calculated columns and measures
- Using DAX (Data Analysis Expressions) for advanced calculations
Day 2: Visualizations and Interactivity
- Creating common visualizations (bar charts, line charts, etc.)
- Customizing visualizations for better insights
- Adding interactivity to reports and dashboards
- Implementing drill-through actions for detailed analysis
- The Art of Storytelling with Data
- Principles of Effective Data Storytelling
- Importance of narrative in data presentations
- Building a cohesive narrative in Power BI
- Using bookmarks and storytelling features
Week 11: Power BI for Real-Time Analytics and Advanced Features
Day 1: Real-Time Dashboards
- Setting up real-time data streaming in Power BI
- Creating dashboards for live data monitoring
Day 2: Advanced Features and Custom Visuals
- Exploring custom visuals and visuals from the marketplace
- Leveraging advanced features like forecasting and clustering
- Case Studies and Discussion
- Reviewing case studies of effective Power BI usage
- Ǫ&A and discussions on best practices in storytelling with data
Conclusion:
Don’t let this opportunity go away! Make your first step and gear yourself with skill set required to advance your career in Data Analytics. The course ‘Data Analytics Training‘ is designed to equip students with various skills of analyzing, interpreting and visualizing data effectively with effectively utilizing the use of Excel, Python, SQL and Power BI. Excel provides foundational knowledge for data management and analytics. Python introduces programming for advance analysis and machine learning. SQL is important for managing databases and large datasets querying, while Power BI allows strong data visualization and business intelligence reporting.
Whether you are a beginner looking for gaining hands- on- experience in this field or a professional looking to enhance your skills, the course is best suited for all, welcoming everyone.
Enroll now and be the first to gain the opportunity to brighten up your career in the field of Data Analyst. Join the Data Analyst Training Course now and do kickstart your learning journey to become a proficient data analyst. Keep Learning, Keep Going!
FAQ’s:
1. How can I learn data analytics on my own?
In order to learn data analytics yourself, follow the certain steps:
- Explore job opportunities & requirements for Data analyst roles.
- Upskill your mathematical and statistical skills.
- Start learning through structured online courses, tutorials and online resources.
- Practice regularly tools such as SQL, Excel, Python/R, Tableau or PowerBI for data analysis, manipulation and visualization.
2. What is the use of Python, SQL and PowerBI in Data analytics?
In Data analytics, Python serves as introducing programming using advanced functions and algorithms for advanced data processing and analysis. SQL is used to query and manage large datasets. While Power BI is a tool used for strong Data visualization & analysis for Business Intelligence.
3. Is data analytics difficult to learn?
Data analytics might be seen as challenging if you are a beginner especially if you are from non- technical background, but with proper time, dedication, approach ,resources and efforts, it’s definitely possible to overcome difficulty and become proficient in this field.
4. When can I access recorded session of the class, incase miss the live class?
The recorded session of the class will get uploaded in 2 working days.
5. How to contact for any query?
In case of any query, you may call us on our toll-free number: (+91) (0)8069289001 or Drop us an email at [email protected]
|
.webp){kind=small}
