Best Data Analysis Course in Delhi | Learn Data Analytics with Python & SQL

Types of Data: Structured vs unstructured data, Primary vs secondary data
Data Collection Methods: Surveys, experiments, observational data, web scraping, databases, and APIs
Data Cleaning: Handling missing values, Dealing with outliers, Normalization and standardization
Data Transformation: Aggregating and summarizing data, Feature engineering, Data encoding

Descriptive Statistics: Measures of central tendency, Measures of spread, Frequency distributions
Probability Basics: Probability distributions, Conditional probability, Bayes’ theorem
Inferential Statistics: Sampling methods, Hypothesis testing, Regression analysis basics

Exploratory Data Analysis (EDA): Visualization techniques, Detecting trends and patterns, Identifying outliers
Correlation and Causation: Pearson and Spearman correlation, Causality vs correlation
Regression Analysis: Simple linear regression, Multiple regression analysis, Model evaluation
Classification and Clustering: Supervised learning (decision trees, k-nearest neighbors), Unsupervised learning (k-means, hierarchical clustering)
Time Series Analysis: Forecasting models, Trend and seasonality analysis

Importance of Visualization: Why visualize data? Types of visualizations
Visualization Tools: Matplotlib, Seaborn, ggplot2, Excel, Power BI
Creating Effective Visualizations: Bar charts, histograms, scatter plots, dashboards
Interpreting Visualizations: Analyzing data through visual representations

Hypothesis Testing: Null hypothesis, Type I/II errors
Statistical Tests: t-tests, chi-square tests, ANOVA, Z-tests
Regression Analysis: Simple and multiple regression, Interpretation of coefficients
Correlation and Causation: Pearson and Spearman rank correlation

Time Series Analysis: Introduction, Trend and seasonality, Forecasting with ARIMA
Classification and Clustering: K-means, Decision trees, Random Forests, SVM
Dimensionality Reduction: PCA, Feature selection techniques

Introduction to SQL for Data Analysis: Basic queries, Aggregate functions, Subqueries
NoSQL Databases: Working with MongoDB, Cassandra

Project-Based Learning: Hands-on projects, Real-world datasets (Kaggle, government data)
Reporting and Communication: Presenting findings, Communicating insights

Best Data Analysis Course