Unlock the power of data with our Data Analyst and Data Science course. Learn to analyze, visualize, and interpret data using real-world tools like Python, Excel, SQL, and machine learning. Gain hands-on experience and industry-ready skills to launch or advance your data career.
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Data is at the heart of modern business, technology, and innovation. Learning data analysis and data science empowers you to extract meaningful insights from raw information, make data-driven decisions, and solve real-world problems. These skills are in high demand across industries like finance, healthcare, marketing, tech, and more.
Whether you’re starting your career, switching fields, or aiming to advance in your current role, mastering data tools like Python, SQL, Excel, and machine learning can unlock a world of opportunities. It’s not just about numbers — it’s about telling stories with data and driving impact.
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Get data from Google Analytics: Learn to set up and navigate Google Analytics 4 (GA4) properties, understand event-based data models, configure custom events, and integrate with other Google services like BigQuery for advanced analysis.
Track user activity (clicks, scrolls, time on page): Implement precise tracking mechanisms for various user interactions, including button clicks, page scrolls, video engagement, form submissions, and time spent on specific page elements. Understand the importance of user journey mapping.
Use web scraping & APIs to gather data: Master techniques for extracting data from websites using libraries like Beautiful Soup and Scrapy (for Python), and learn to interact with various RESTful APIs (e.g., social media APIs, public data APIs) to programmatically collect data for your projects.
Traffic: Track website visits, unique visitors, and sources like organic, paid, and referral traffic.
Bounce & Engagement Rate: Understand the difference between these metrics to assess how well your content holds a user’s attention.
Session & Engagement Time: Measure how long users are actively interacting with your site.
Conversion Rate: Calculate and optimize conversions for goals like purchases or sign-ups.
Funnel Analysis: Map a user’s journey to find drop-off points and areas for improvement.
A/B Testing: Design and interpret tests to make data-driven decisions on website elements.
User Segmentation: Group users by device, location, or behavior to personalize experiences and marketing.
Basic statistics: Develop a strong foundation in descriptive statistics (mean, median, mode, standard deviation), inferential statistics (hypothesis testing, confidence intervals), and probability distributions relevant to data analysis.
Machine learning (recommendations, predictions, classification): Get an introduction to core ML concepts, including supervised vs. unsupervised learning. Explore common algorithms like linear regression, logistic regression, decision trees, and clustering for tasks such as predicting outcomes, classifying users, and building recommendation engines.
Text analysis (user reviews, comments, sentiment analysis): Learn techniques to extract insights from unstructured text data, including keyword extraction, topic modeling, and sentiment analysis to understand customer opinions and feedback.
Python & SQL: Use these for advanced data processing and analysis. Python is great for data manipulation, statistics, and machine learning, while SQL is essential for querying and managing databases.
Excel: A go-to tool for quick calculations and data summaries. It’s ideal for rapid data exploration using functions like Pivot Tables and VLOOKUP.
Tableau / Power BI: These are used to create interactive dashboards and compelling visualizations from various data sources, allowing for independent data exploration.
Google Analytics & Hotjar: These tools are for tracking user behavior. Google Analytics provides reporting and exploration, while Hotjar offers qualitative insights through heatmaps and session recordings.
Turn Data into Visuals: The goal is to create clear visuals that support data-driven decisions.
Create Charts & Graphs: Learn to choose the right chart (e.g., line charts for trends, bar charts for comparisons) to tell a data story effectively.
Build Dashboards: Design dashboards that provide a holistic view of performance and monitor key metrics (KPIs) for quick insights.
Focus on Clarity: Remember that the purpose of visualization is not just aesthetics, but to facilitate understanding and enable action.
Improve User Experience (UX): Use data to identify issues, understand user journeys, and make informed design decisions for websites or apps.
Analyze Behavior: Use tools like heatmaps and session recordings to understand how users interact with your site, then optimize navigation and content for better engagement.
Personalize: Deliver tailored content, offers, or layouts to individual users based on their behavior and preferences.
Recommend: Learn about recommendation systems (like collaborative filtering) to suggest products or content dynamically, boosting engagement and sales.
A Data Analyst is a professional who collects, organizes, and analyzes data to help companies make informed decisions. They turn raw numbers into meaningful insights by identifying trends, patterns, and problems. Data Analysts use tools like Excel, SQL, Python, and visualization software (such as Tableau or Power BI) to present data in clear and understandable ways. They play a key role in improving business strategies, optimizing performance, and supporting data-driven decision-making across different industries.
Data Science is the field of extracting knowledge and insights from structured and unstructured data using scientific methods, algorithms, and advanced analytics. It stands as a multidisciplinary domain, merging concepts from programming (like Python/R), statistics, and machine learning to rigorously examine data. The process moves beyond basic reporting to understand complex trends, build predictive models, and ultimately support high-level, strategic decision-making by transforming raw, messy data into clear, actionable business value.
Excel, Advanced Excel & VBA Macros
Boost your productivity from basic to pro-level!
Excel: Learn formulas, charts, and PivotTables
Advanced Excel: Master VLOOKUP, dashboards & data tools
VBA & Macros: Automate tasks and build custom tools
Perfect for data professionals, analysts & smart Excel users!
I turn raw data into actionable insights using Python, SQL, and Power BI.
Python automates tasks, cleans data, and builds models with Pandas, NumPy, and Matplotlib.
SQL handles data extraction, while Power BI transforms it into interactive dashboards.
Explore my projects, internships, and placement preparation focused on Data Analytics and Data Science.
Hands-on experience with real-world data, tools like Python, SQL, and Power BI, and end-to-end analytics solutions.
Focused on building skills, solving problems, and preparing for roles in data-driven industries.
Gain hands-on skills with 100% practical and lab-based training in hardware setup, networking, troubleshooting, and real-time infrastructure tasks.
Thanked mentor for helping retain a job and gain clarity in accounting despite non-commerce background; rated 5/5
Our expert instructors don’t just teach — they mentor you every step of the way, helping you build an impressive work portfolio and guiding you toward exciting career opportunities!"
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