Training on Statistical Analysis Using Python

Statistical Analysis Using Python: Beginner to Advanced

Python is an open-source programming language that provides a wide variety of statistical and graphical techniques.  Python has “become the de-facto standard for writing statistical software among statisticians. This Training on Statistical Data Analysis using Python will give you a solid foundation in creating statistical analysis solutions using the R language, and how to carry out a range of commonly used analytical processes.

Target Participants 

This Training on Statistical Data Analysis using Python is intended for Data Scientists, Data Analysts, Business Intelligence Analysts and any other professional who want to explore the vast range of analytical and graphical capabilities of Python.

Course Duration 

Online                         30 Days

Classroom-based     14 Days

Training Objectives

🔹 Develop Python code for cleaning and preparing data for analysis – including handling missing values, formatting, normalizing, and binning data

🔹 Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy.

🔹 Data Visualization using Matplotlib.pyplot and Seaborn Libraries in Pandas DataFrame

🔹 Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

🔹 Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Content

Module 1: Beginner-friendly

🔹 Installing Anaconda

🔹 Getting Started with Jupyter Notebook

🔹 Python libraries like numPy, Matplotlib, Pandas, sciPy, Seaborn, and Scikit-learn.

🔹 Variables and Data types

🔹 Pandas Series

🔹 Pandas DataFrame

🔹 Create a DataFrame

🔹 Reading a .csv & xls File(Importing External Data)

🔹 Enter Data and Create Variables using Pandas DataFrame

🔹 Dealing with Rows and Columns

Modules 2: Data Manipulation in Pandas DataFrame

🔹 Converting Numeric Variables into String Variables in Pandas DataFrame

🔹 Converting String Variables into Numeric Variables in Pandas DataFrame

🔹 Converting String Variables into Categorical Variables in Pandas DataFrame

🔹 Adding Labels to Variables in Pandas DataFrame

🔹 Recoding Categorical Variables in a Pandas Dataframe

🔹 Adding New Variables on DataFrames In Pandas

🔹 Renaming variables in Pandas DataFrame

Module 3: Descriptive Statistics in Pandas DataFrame

🔹 [Theory] Measurement of Central Tendency

🔹 [Theory] Measurement of Dispersion

🔹 Find the mean, mode, median, Std, Skewness, Kurtosis

🔹 Frequency Tables

Module 4: Data Visualization in Pandas DataFrame 

🔹 Histogram using matplotlib.pyplot

🔹 Boxt Plot matplotlib.pyplot

🔹 Line and Scatter Plots using matplotlib.pyplot

🔹 Bar Plot using matplotlib.pyplot

🔹 Pie Plot using matplotlib.pyplot

🔹 Displot group of plots using Seaborn

🔹 Relplot group of plots using Seaborn

Module 5: Inferential Statistics and Hypotheis Testing Part 1[Theory]

🔹 Basics of Hypothesis Testing

🔹 Statistical and Practical Significance

🔹 Null and Alternative Hypothesis

🔹 Type of Errors-Type I and Type II Errors

🔹 The p-Value

🔹 The Rejection and Acceptance Criteria

Module 6: Inferential Statistics Using Pandas DataFrame-Part 2

🔹 Cross-Tabulation

🔹 Chi-Square Test

🔹 T-Tests (one Sample, Two Samples & Paired)

🔹 ANOVA Test

🔹 Pairwise comparisons of means with equal variances

🔹 Pearson Correlation Test

🔹 Partial Correlation Test

🔹 Spearman Test

Module 7: Inferential Statistics-Part 3

🔹 Linear Regression Analysis

🔹 Multiple Regression Analysis

🔹 Bivariate Analysis

🔹 Multivariate Analysis

🔹 Binary Logistic

🔹 Ordinal Logistic Regression

Training Approach

This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies. Training manuals and additional reference materials are provided to the participants.

Certification

Upon successful completion of this course, participants will be issued with a certificate.

Tailor-Made Course

We can also do this as a tailor-made course to meet organization-wide needs. We can also do this as a tailor-made course to meet organization-wide needs. A training needs assessment will be done on the training participants to collect data on the existing skills, knowledge gaps, training expectations, and tailor-made needs.