DIPLOMA IN DATA ANALYSIS ( S-AS-05 )

1. Introduction to Data Analysis
   • Overview of data analysis and its significance
   • Introduction to common tools and techniques
   • Basics of data visualization
2. Data Acquisition and Preprocessing
   • Data collection methods
   • Data cleaning and preprocessing techniques
   • Handling missing data
   • Data integration and transformation
3. Exploratory Data Analysis (EDA)
   • Descriptive statistics
   • Data summarization and visualization
   • Univariate, bivariate, and multivariate analysis
   • Outlier detection and treatment
4. Data Visualization
   • Introduction to data visualization libraries (e.g., Matplotlib, Seaborn)
   • Plotting histograms, scatter plots, bar charts, and box plots
   • Customizing plots for effective communication
5. Statistical Analysis
   • Probability distributions
   • Hypothesis testing (e.g., t-tests, chi-square tests)
   • Correlation and regression analysis
   • ANOVA (Analysis of Variance)
6. Data Mining Techniques
   • Introduction to machine learning algorithms
   • Supervised learning (classification and regression)
   • Unsupervised learning (clustering, dimensionality reduction)
   • Evaluation metrics
7. Time Series Analysis
   • Introduction to time series data
   • Trend analysis
   • Seasonality and periodicity
   • Forecasting techniques
8. Text Analytics
   • Preprocessing text data (tokenization, stemming, lemmatization)
   • Text mining techniques (topic modeling, sentiment analysis)
   • Natural Language Processing (NLP) libraries
9. Big Data Analytics
   • Introduction to big data concepts
   • Distributed computing frameworks (e.g., Hadoop, Spark)
   • Handling large datasets
10. Data Ethics and Privacy
    • Ethical considerations in data analysis
    • Data privacy laws and regulations
    • Ensuring data security and confidentiality
11. Data Visualization and Dashboarding Tools
    • Introduction to data visualization tools (e.g., Tableau, Power BI)
    • Building interactive dashboards
    • Storytelling with data
12. Project Work
    • Applying learned concepts to real-world datasets
    • Developing analytical solutions to solve business problems
    • Presenting findings and insights

FOR GRADUATION STUDENTS OR APPERING STUDENTS