Duration
3 days
Pre-requisite
Participants should be familiar with python concepts
Course Outline
Topic 1 Data Preparation
- Data Analytics with Pandas
- Pandas DataFrame and Series
- Import and Export Data
- Filter and Slice Data
- Clean Data
Topic 2 Data Transformation
- Join Data
- Transform Data
- Aggregate Data
Topic 3: Data Visualization with Matplotlib
- Basic Plots
- Subplots
- Visualizing Categorical Data
- Visualizing Distributions
Topic 4: Data Visualization with Pandas
- Scatter Plot
- Line Plot
- Bar Plot
- Pie Chart
- Histogram
- Box Plot
- Interactive Plot
Topic 5: Data Visualization with Seaborn
- What is Seaborn
- Styling Setting
- Visualizing Statistical Relationships with Scatter Plot
- Visualizing Categorical Data with Bar Plot
- Visualizing Correlation with Pair Plot and Heatmap
- Visualizing Linear Relationships with Regression
Topic 6: GeoSpatial Visualization with Bokeh
- What is Bokeh
- Plotting with Glyphs
- Google Map API
Topic 7 Regression
- What is Regression?
- Residues and Mean Square Error
Topic 8 Correlation Analysis
- Covariance and Covariance Matrix
- Correlation Coefficient and Correlation Matrix
Topic 9 Time Series Analysis
- Overview of Time Series Analysis
- Visualizing Time Series and its Volatility
- Detecting Outliers
- Normality Test
- Stationary Test
Topic 10 Time Series Modeling
- Components of Time Series
- Time Series Decomposition
- ARIMA Modeling of Time Series
- Time Series Forecasting with StatsModel and Prophet
Topic 11 Introduction to Statistics
- Why Statistics Matter
- Categorical and Quantitative Data
- Descriptive Statistics: Mean and Standard Deviation
- Probability and Conditional Probability
- Bayes Theorem
- Probability Distributions
Topic 12 Frequentist Inference
- What is Hypothesis Testing
- T Statistics
- Student's t-test
- P-Value and Significance Level
- What is Statistical Modeling
- Statistical Modeling with StatsModel
- F Statistics and Distribution
- Goodness of Fit
- ANOVA
Topic 13 Bayesian Inference
- Overview of Bayesian Statistics and Bayesian Inference
- Conjugate Priors - Beta and Gamma Distributions
- Markov Chain Monte Carlo (MCMC) Methods
- Posterior Estimate with PyMC3
- Maximum A Posterior (MAP) Estimation
- Bayesian Linear Regression
Programme scope, delivery arrangements and suitability are confirmed during enquiry.