Data Analytics
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Introduction to Data Analytics
Welcome to our comprehensive data analytics course module. This module is designed to equip you with the skills and knowledge necessary to excel in the field of data analytics. Whether you are a beginner or have some experience, our course will help you understand the fundamentals and advanced concepts of data analytics.
Duration - 4 Months
- Overview of data analytics
- Importance and applications of data analytics
- Data analytics lifecycle
- Setting up the Python environment (Anaconda, Jupyter Notebook, PyCharm)
- Python syntax and structure
- Basic data types and variables
- Control flow (if statements, loops)
- Functions and modules
- Lists, tuples, and sets
- Dictionaries
- Comprehensions (list, dictionary, set)
- Reading data from CSV, Excel, JSON, and SQL databases
- Web scraping with BeautifulSoup and Scrapy
- Accessing APIs with requests
- Introduction to relational databases
- SQL basics (SELECT, INSERT, UPDATE, DELETE)
- Connecting Python to SQL databases using SQLAlchemy
- Handling missing values
- Removing duplicates
- Data transformation (scaling, normalization)
- DataFrames and Series
- Indexing, slicing, and filtering
- Aggregation and grouping
- Objectives of EDA
- Tools and libraries (Pandas, NumPy, Matplotlib, Seaborn)
- Creating plots and charts with Matplotlib
- Advanced visualizations with Seaborn
- Interactive visualizations with Plotly
- Summary statistics (mean, median, mode)
- Measures of dispersion (variance, standard deviation)
- Correlation and covariance
- Basic probability concepts
- Probability distributions (normal, binomial, Poisson)
- Hypothesis testing
- Confidence intervals
- t-tests, chi-square tests, ANOVA
- Simple linear regression
- Multiple linear regression
- Logistic regression
- Overview of machine learning
- Supervised vs. unsupervised learning
- Model evaluation and selection
- Classification algorithms (decision trees, random forest, k-nearest neighbors)
- Regression algorithms (linear regression, polynomial regression)
- Clustering algorithms (k-means, hierarchical clustering)
- Dimensionality reduction (PCA, t-SNE)
- Overview of big data technologies
- Working with Hadoop and Spark
- Generating reports with Jupyter Notebook
- Using BI tools (Tableau, Power BI)
- Designing and implementing a data analytics project
- Collecting, cleaning, and analyzing data
- Building predictive models
- Visualizing and presenting results