This comprehensive data analytics level 1 course focusing on Power BI and MS Excel involves covering fundamental concepts, practical skills, and advanced techniques. Here’s a structured outline for such a course:
Learning Objectives
In this course, you will learn:
- Basic concepts and terminology of data analytics.
- Process of data collection, storage, and retrieval.
- Data cleaning and preprocessing techniques.
- Common data analysis tools e.g. Excel, Python, or R.
- Machine learning and its applications in data analytics.
- Problem-solving skills to interpret and analyze data.
Course Modules
MODULE 1
Introduction to Data Analytics
- Lesson 1: Understanding Data Analytics and its Importance
- Lesson 2: Overview of Data Analytics Lifecycle
- Lesson 3: Introduction to Data Analytics Tools and Technologies
MODULE 2
Data Collection and Preparation
- Lesson 1: Data Collection Methods and Sources
- Lesson 2: Data Cleaning and Preprocessing Techniques
- Lesson 3: Exploratory Data Analysis (EDA)
- Lesson 4: Data Integration and Transformation
MODULE 3
Data Exploration and Visualization
- Lesson 1: Introduction to Data Visualization Tools (e.g., Matplotlib, Seaborn, Tableau)
- Lesson 2: Exploring Data Patterns and Trends
- Lesson 3: Visualizing Data with Charts, Graphs, and Dashboards
- Lesson 4: Best Practices for Data Visualization
MODULE 4
Statistical Analysis for Data Analytics
- Lesson 1: Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
- Lesson 2: Inferential Statistics (Hypothesis Testing, Confidence Intervals)
- Lesson 3: Correlation and Regression Analysis
- Lesson 4: Time Series Analysis
MODULE 5
Predictive Analytics
- Lesson 1: Introduction to Predictive Modeling
- Lesson 2: Linear and Logistic Regression
- Lesson 3: Decision Trees and Random Forests
- Lesson 4: Model Evaluation and Validation Techniques
MODULE 6
Machine Learning for Data Analytics
- Lesson 1: Introduction to Machine Learning Algorithms
- Lesson 2: Supervised, Unsupervised, and Semi-Supervised Learning
- Lesson 3: Clustering Algorithms (K-Means, Hierarchical Clustering)
- Lesson 4: Dimensionality Reduction Techniques (PCA, t-SNE)
MODULE 7
Big Data Analytics
- Lesson 1: Introduction to Big Data Technologies (Hadoop, Spark)
- Lesson 2: Processing and Analyzing Big Data with Spark
- Lesson 3: Working with Distributed Datasets
- Lesson 4: Streaming Analytics with Apache Kafka
MODULE 8
Data Mining and Pattern Recognition
- Lesson 1: Introduction to Data Mining Techniques
- Lesson 2: Association Rule Mining (Apriori Algorithm)
- Lesson 3: Cluster Analysis (K-Means, DBSCAN)
- Lesson 4: Anomaly Detection and Outlier Analysis
MODULE 9
Text Analytics and Natural Language Processing (NLP)
- Lesson 1: Introduction to Text Analytics
- Lesson 2: Preprocessing Text Data (Tokenization, Stemming, Lemmatization)
- Lesson 3: Sentiment Analysis
- Lesson 4: Named Entity Recognition (NER) and Text Classification
MODULE 10
Advanced Data Analytics Topics
- Lesson 1: Deep Learning for Data Analytics
- Lesson 2: Reinforcement Learning
- Lesson 3: Graph Analytics
- Lesson 4: Time Series Forecasting with Neural Networks
MODULE 11
Data Ethics and Privacy
- Lesson 1: Understanding Ethical Issues in Data Analytics
- Lesson 2: Privacy Concerns and Data Protection Regulations (GDPR, CCPA)
- Lesson 3: Ethical Decision Making in Data Analytics Projects
- Conclusion
- Project
- Assessment
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