Data Analysis
Make Data-Driven Business Decisions. Achieve fluency in business data strategies in four discipline-specific courses.
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Data Analysis
Grid Tech’s Data Analyst Course is designed to cater to both beginners and professionals aiming to sharpen their skills in data analysis. The program emphasizes the significance of data in contemporary business decision-making, providing hands-on experience with real-world data sets. Participants will be immersed in a variety of data collection methods, including surveys, social media analytics, and online behavior tracking. By the end of the course, students will have acquired a robust foundation in data analytics, enabling them to make informed business decisions based on data-driven insights.
Course Progress
- Beginner
- 3 Total Enrolled
- 40 hours Duration
- Lectures: 1
- Beginner
- 3 Total Enrolled
- 40 hours Duration
- July 7, 2022 Last Updated
- Lectures: 1
- Students: Max 110
- Level: Beginner
- Language: English
- Certificate: Yes
- Students: Max 110
- Level: Beginner
- Language: English
- Certificate: Yes
Introduction to Python & Machine Learning (ML):
- Overview: Understanding the role of Python and ML in the Software Industry.
- Python Basics: Classes, Objects, Data Types, and Control Flow.
- ML Introduction: Gaining a foundational understanding of Machine Learning.
Data Manipulation and Visualization:
- Tools: Learning to use NumPy, Pandas, and Matplotlib for data handling and visualization.
- Linear Algebra Fundamentals: Exploring points, lines, planes, and their roles in data analysis.
Understanding Distances and Statistics in Data Analysis:
- Distance Metrics: Delving into Euclidean, Angular, Directed, and Cosine distances.
- Statistics: Covering basics to advanced concepts including distributions, correlations, and intervals.
Advanced Data Analysis Techniques:
- Dimensionality Reduction: Practical application of PCA with dataset visualization.
- Regression Models: Building and understanding Linear and Logistic Regression models.
- Optimization: Learning various optimization techniques including Gradient Descent and Ada Boosting.
- Classification Algorithms: In-depth study of KNN, Naive Bayes, and Decision Trees.
Unsupervised Learning and Clustering:
- Introduction: Gaining an understanding of Unsupervised Learning.
- Clustering Techniques: Practical application of K Means, DB-scan, and Algometric Clustering.
Practical Applications and Tools:
- Excel: Mastering advanced formulas, chart manipulation, and optimization.
- Power BI: Exploring data modeling, DAX, and dashboard formatting.
- SQL: Understanding SQL syntax, data types, operators, and queries.
- Report Automation: Learning to automate report generation and delivery using Python and SQL.
Real-World Project and Practice:
- Hands-On Project: Applying learned skills in a live project scenario.
- Hacker Rank Exercises: Enhancing skills through targeted practice exercises.
By the end of the Grid Tech Data Analyst Course, participants will be well-equipped to analyze data effectively, draw meaningful insights, and contribute to data-driven decision-making processes within their organizations.