Corporate Introduction to Data Science Training Course

1. Introduction to data science

1. Definition and importance
2. Key concepts and skills

2. Analytics landscape

1. Overview of analytics types
2. Role in decision-making
3. Impact on various industries

3. Life cycle of a data science project

1. Stages and milestones
2. Project management techniques
3. Common challenges and solutions

4. Data science tools & technologies

1. Overview of software and languages
2. Comparison of data science platforms
3. Introduction to cloud computing for data science

1. Measures of central tendency

1. Calculating mean, median, and mode
2. Applications in data analysis
3. Limitations and considerations

2. Measures of dispersion

1. Understanding range, variance, and standard deviation
2. Importance in data interpretation
3. Calculating and comparing dispersion measures

3. Descriptive statistics

1. Role in data summarization
2. Visualization techniques

4. Probability basics

1. Fundamental concepts and rules
2. Applications in decision-making

5. Marginal probability

1. Definition and calculation
2. Examples and applications
3. Differences from conditional probability

6. Bayes theorem

1. Explanation and formula
2. Bayesian vs. frequentist approaches

7. Probability distributions

1. Types and characteristics
2. Selecting appropriate distributions
3. Modeling real-world phenomena

8. Hypothesis testing

1. Formulating and testing hypotheses
2. Error types and power of a test

1. Install Anaconda

1. Step-by-step installation guide
2. Configuring environments
3. Troubleshooting common issues

2. Data types & variables

1. Overview of Python data types
2. Using variables effectively
3. Type conversion and manipulation

3. String & regular expressions

1. Basics of string manipulation
2. Introduction to regular expressions

1. Python list

1. Creating and manipulating lists
2. List comprehensions and operations

2. Python dictionaries

1. Dictionary basics and usage
2. Advanced techniques and patterns

3. Python set

1. Set operations 
2. Comparing sets and lists
3. Efficiency and applications

4. Python tuple

1. Understanding tuples and their usage
2. Tuple operations and methods
3. Applications in data science

5. Comprehensions

1. List, set, and dictionary comprehensions
2. Writing efficient and readable code

1. For loop

1. Basics and syntax
2. Advanced for loop patterns
3. Nested loops and iterations

2. While loop

1. Understanding while loops
2. Controlling loop execution

3. Break statement

1. Controlling flow in loops
2. Scenarios and examples

4. Next statements

1. Using next for iteration control
2. Differences from break

5. Repeat statement

1. Introduction and usage
2. Repeat vs. while loops
3. Applications in data manipulation

6. If, if…else statements

1. Making decisions in Python
2. Complex conditional structures

7. Switch statement

1. Simulating switch-case in Python
2. Alternatives and approaches

1. Writing your functions (UDF)

1. Defining custom functions
2. Parameters and return values

2. Calling Python functions

1. Basic and advanced calling techniques
2. Handling arguments and return values
3. Debugging and troubleshooting

3. Functions with arguments

1. Understanding arguments and parameters
2. Keyword vs. positional arguments
3. Advanced argument techniques

4. Calling Python functions by passing arguments

1. Effective argument passing
2. Using *args and **kwargs

5. Lambda functions

1. Introduction to lambda expressions
2. Comparing lambda with regular functions

6. Classes & objects

1. Basics of object-oriented programming
2. Defining classes and creating instances
3. Advanced OOP concepts and patterns

1. Reading files with Python

1. Opening and reading text files
2. Handling CSV and Excel files

2. Writing files from Python

1. Writing to text and CSV files
2. Generating Excel files programmatically
3. Ensuring data integrity and formatting

3. Reading files using Pandas library

1. Introduction to Pandas for file input
2. Reading various file formats
3. Dataframe operations for data ingestion

4. Saving data using Pandas library

1. Exporting data to different formats
2. Customizing output parameters
3. Efficient data storage techniques

1. Clean & prepare datasets

1. Identifying and handling missing data
2. Data type conversions and normalization
3. Feature engineering and preparation

2. Manipulate DataFrame

1. Advanced DataFrame manipulation techniques
2. Merging, joining, and concatenating data
3. Aggregation and grouping for analysis

3. Summarize data

1. Generating summary statistics
2. Understanding data through descriptive analytics
3. Visual summary techniques

4. Churn insights from data

1. Pattern recognition and anomaly detection
2. Insight generation methodologies

1. Charts using Matplotlib

1. Basics of plotting with Matplotlib
2. Customizing plots and charts
3. Complex visualizations and layouts

2. Charts using Seaborn

1. Introduction to Seaborn for statistical plots
2. Advanced data visualization techniques

3. Charts using ggplot

1. ggplot basics and philosophy
2. Building plots layer by layer
3. Comparing ggplot with Matplotlib and Seaborn

1. ANOVA

1. Understanding analysis of variance
2. Conducting ANOVA tests
3. Interpreting results for decision-making

2. Linear regression (OLS)

1. Building linear models with OLS
2. Diagnostics and assumptions testing

3. Principal component analysis

1. Dimensionality reduction techniques
2. PCA for feature extraction
3. Visualization and interpretation of components

4. Factor analysis

1. Basics and methodology of factor analysis
2. Application in uncovering latent variables
3. Factor extraction and rotation techniques

5. Logistic regression (MLE)

1. Using logistic regression for classification
2. Model building and evaluation

6. K-nearest neighbor algorithm

1. Understanding KNN and its applications
2. Choosing the right value of K
3. Scaling and preprocessing for KNN

7. Decision tree

1. Constructing decision trees for classification and regression
2. Tree pruning and complexity control
3. Visualizing and interpreting decision trees

1. Understand time series data

1. Characteristics of time series data
2. Decomposition of time series
3. Seasonality and trend analysis

2. Visualizing time series components

1. Time series visualization techniques
2. Identifying patterns and cycles
3. Tools and libraries for time series visualization

3. Exponential smoothing

1. Simple and double exponential smoothing models
2. Tuning and optimization of parameters
3. Forecasting with exponential smoothing

4. Holt's model

1. Introduction to Holt's linear trend method
2. Application in trend analysis
3. Combining level and trend components

5. Holt-Winter's model

1. Capturing seasonality with Holt-Winters
2. Parameter selection and model fitting

6. ARIMA

1. Basics of ARIMA modeling
2. Model identification and fitting
3. Diagnostics and forecasting with ARIMA

1. What is machine learning?

1. Defining machine learning and its scope
2. Types of machine learning models
3. Applications and examples in the industry

2. Supervised learning

1. Overview of supervised learning techniques
2. Building and evaluating models

3. Unsupervised learning

1. Exploring unsupervised learning algorithms
2. Clustering and dimensionality reduction

4. Using Scikit-learn

1. Introduction to Scikit-learn for machine learning
2. Preprocessing and model selection
3. Building pipelines and model evaluation

5. Scikit-learn classes

1. Understanding the Scikit-learn API
2. Popular classes and their applications
3. Advanced techniques and customizations