Advanced Data Analytics Program
The Advanced Data Analytics Program offers extensive training in essential tools such as SQL, Python, Advanced Excel, Tableau, and Power BI, equipping students with the skills to analyze large datasets, perform predictive analytics, and develop interactive dashboards. Students will gain hands-on experience with real-world data, learning how to extract valuable insights, visualize trends, and make data-driven decisions. Upon completion, graduates will be prepared for high-demand roles such as Data Analyst, Data Scientist, Business Intelligence Analyst, and Analytics Consultant. With data becoming a core driver of business strategy in industries like finance, healthcare, marketing, and technology, this program opens up a wealth of career opportunities, positioning students for success in the rapidly growing field of data analytics.
Course Curriculum
Module 1: Introduction to Data Analytics & Tools Overview
- Introduction to Data Analytics concepts and its importance in decision-making.
- Overview of key tools: SQL, Python, Advanced Excel, Power BI, and Tableau.
- The role of a Data Analyst and career opportunities.
- Introduction to Data Wrangling, Data Cleaning, and Data Transformation.
- Overview of real-time project and its applications in the industry.
Module 2: SQL for Data Analysis
- Basics of SQL: SELECT, JOIN, WHERE, and aggregate functions.
- Writing complex queries to extract and manipulate data.
- Data filtering, sorting, and grouping.
- Subqueries, unions, and set operations in SQL.
- Advanced SQL functions for data analysis (window functions, CTEs).
- Real-time project: Analyzing and reporting data using SQL queries.
Module 3: Python for Data Analytics
- Introduction to Python and essential libraries (Pandas, NumPy, Matplotlib).
- Data cleaning and manipulation with Pandas.
- Data visualization techniques using Python (Matplotlib and Seaborn).
- Performing statistical analysis with Python (hypothesis testing, regression).
- Introduction to machine learning for predictive analytics.
- Real-time project: Building data models and performing analysis using Python.
Module 4: Advanced Excel for Data Analysis
- Advanced Excel functions: VLOOKUP, INDEX/MATCH, and pivot tables.
- Working with Excel charts, data visualization, and conditional formatting.
- Power Query and Power Pivot for advanced data manipulation.
- Automation of repetitive tasks using Excel macros and VBA.
- Data analysis techniques for large datasets using Excel.
- Real-time project: Analyzing business data and creating actionable insights using Excel.
Module 5: Reporting and Visualization with Power BI & Tableau
- Introduction to Power BI: data import, data cleaning, and building dashboards.
- Data modeling and creating visualizations in Power BI.
- Advanced Power BI features: DAX functions, calculated columns, and measures.
- Introduction to Tableau: Connecting data sources and building interactive dashboards.
- Best practices for creating compelling visualizations and reports in Tableau.
- Real-time project: Building dynamic, interactive reports and dashboards with Power BI/Tableau.
Module 6: Career Readiness, Interview Preparation & Real-Time Project
- Resume building tailored to Data Analytics roles.
- Job search strategies and optimizing LinkedIn profiles for analytics roles.
- Interview preparation: Common interview questions and technical assessments.
- Mock interviews with feedback to improve interview performance.
- Real-time project: End-to-end data analytics project implementation.
- Interview assistance and career guidance to help land a Data Analyst job.
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Python,SQL,Unix,Adv Excel,PowerBI
Our Data Analytics for Banking Project leverages powerful technologies such as SQL, Python, Advanced Excel, Tableau, and Power BI to drive actionable insights and improve decision-making processes in the banking sector. This project focuses on analyzing large volumes of financial data, customer behavior, transaction patterns, and market trends to optimize banking operations and enhance customer experiences.
Stock Market Optimization Using Advanced Technologies
Our Data Analytics for Stock Market Project utilizes advanced technologies like SQL, Python, Advanced Excel, Tableau, and Power BI to provide actionable insights and improve decision-making in the stock market. Using SQL, we query and extract financial data, market trends, and trading information for in-depth analysis. Python is applied for data cleaning, predictive modeling, and algorithmic trading strategies, forecasting stock prices and market movements. Advanced Excel is used for portfolio management, financial analysis, and report generation, while Tableau and Power BI create interactive dashboards to visualize key market metrics such as stock performance, volatility, and trading volume. This data-driven approach helps investors and analysts make informed decisions, optimize trading strategies, and enhance portfolio performance.
Real Money Gaming Optimization Using Advanced Technologies
Our Real Money Gaming Analytics Project utilizes powerful technologies such as SQL, Python, Advanced Excel, Tableau, and Power BI to optimize gaming performance and enhance user engagement. SQL is used to analyze large datasets of player activities, in-game transactions, and outcomes, providing a detailed view of gaming patterns. Python facilitates data cleaning, predictive analysis, and fraud detection to ensure fair play and optimize gaming strategies. Advanced Excel supports financial analysis, revenue tracking, and reporting, while Tableau and Power BI are used to create dynamic dashboards, visualizing key metrics like player retention rates, revenue streams, and game success. This data-centric approach enables gaming companies to improve player experiences, boost profitability, and maintain fairness, all while driving growth in the competitive real money gaming market.
Healthcare Project Using Advanced Technologies
Our Data Analytics for Healthcare Project leverages advanced technologies such as SQL, Python, Advanced Excel, Tableau, and Power BI to enhance decision-making and operational efficiency in the healthcare sector. By using SQL, we query and extract patient data, medical histories, and billing information for comprehensive analysis. Python is applied for data cleaning, predictive analytics, and forecasting patient admissions and treatment outcomes. Advanced Excel is utilized for financial planning and report generation, while Tableau and Power BI create interactive dashboards that visualize key healthcare metrics such as patient satisfaction, treatment success, and hospital performance. This data-driven approach helps healthcare providers optimize resources, improve patient care, and make informed decisions for better service delivery.