Description
Here’s a detailed outline for a Data Analytics Course for Tandagrid Academy, including what students will learn and the course duration:
Data Analytics Course Outline
Course Duration:
- 12 Weeks (3 Months)
- Format: 3 classes per week (2 hours per class)
Module 1: Introduction to Data Analytics
- Week 1
- What is data analytics?
- Understanding the role of data in decision-making
- The difference between data analytics, data science, and business intelligence
- Overview of the data analytics process (data collection, cleaning, analysis, visualization)
Module 2: Data Collection and Cleaning
- Weeks 2 & 3
- Data types and data sources (structured, unstructured, relational databases, APIs)
- Introduction to SQL for data querying
- Techniques for data cleaning (handling missing data, outliers)
- Data normalization and transformation
- Best practices for data quality
Module 3: Descriptive Statistics and Data Exploration
- Weeks 4 & 5
- Understanding basic statistical concepts (mean, median, mode, variance, standard deviation)
- Data visualization techniques (bar charts, histograms, scatter plots)
- Introduction to Excel and Google Sheets for data analysis
- Using Python for data analysis (NumPy, Pandas)
- Exploratory Data Analysis (EDA) for identifying patterns
Module 4: Data Visualization and Dashboards
- Weeks 6 & 7
- Principles of effective data visualization
- Introduction to tools for data visualization (Tableau, Power BI, Matplotlib, Seaborn)
- Creating interactive dashboards
- Best practices for presenting data to stakeholders
- Storytelling with data: transforming insights into actionable recommendations
Module 5: Statistical Analysis and Hypothesis Testing
- Weeks 8 & 9
- Introduction to inferential statistics
- Hypothesis testing: t-tests, chi-square tests, ANOVA
- Correlation and regression analysis
- Time series analysis
- Hands-on project: analyzing datasets to draw conclusions
Module 6: Predictive Analytics and Machine Learning Basics
- Weeks 10 & 11
- Introduction to predictive modeling
- Understanding machine learning concepts (supervised vs unsupervised learning)
- Regression models (linear regression, logistic regression)
- Introduction to classification models (decision trees, random forests)
- Model evaluation techniques (accuracy, precision, recall)
Module 7: Capstone Project and Portfolio Building
- Week 12
- Final capstone project: End-to-end data analysis (collecting, cleaning, analyzing, and visualizing real-world data)
- Building a data analytics portfolio
- Preparing for job interviews and freelancing opportunities
- Ethical considerations in data analytics
Tools Covered:
- Excel, Google Sheets, SQL, Python (Pandas, NumPy, Matplotlib), Tableau, Power BI
Assessment and Certification:
- Project submissions and peer feedback
- Final capstone project evaluation
- Certification upon course completion
This 12-week course will provide students with a thorough understanding of the data analytics process, from data collection and cleaning to visualization and predictive analysis, equipping them with essential tools and techniques for a career in data analytics.
Reviews
There are no reviews yet.