Object Automation teach school and college students on current technologies and help to them toget expertise in the respective field of study.

1. Python for Data Science
Statistics For Data Science
Data Analysis with Python
Data Visualization with
python
Machine Learning
Information Technology Solutions
STATISTICS FOR DATA SCIENCE
Module 1 –Analytics Problem Solving
 Introduction
 Business Problem – Definition &
Understanding
 Preparing Data for Analysis
 Model Evaluation and deployment
Module 2 – Inferential Statistics
 Basics of Probability
 Discrete Probability Distribution
 Continuous Probability Distribution
 Central Limit Theorem
Module 3 – Hypotheis Testing
 Concepts of Hypothesis Testing I
 Concepts of Hypothesis Testing II
 Industry Demonstration of
Hypothesis Testing
 Hypothesis Testing Additional
Resources
Object Automation Software Solutions Pvt.
Ltd.
Data Science Foundations
ABOUT THE COURSE
The course covers the
concepts and tools you need
throughout the entire data
science pipeline, In the final
project, you’ll apply the skills
learned by building a data
product using real-world data.
At completion, students will
receive IBM Badge with the
completion certificate.
PERFORMANCE
EVALUATION
Every specialization includes a
hands-on performance
evaluation. You’ll need to
successfully clear the
evaluation process to complete
the specialization.
PYTHON FOR DATA SCIENCE
Module 1 - Python Basics
 Your first program
 Types
 Expressions and Variables
 String Operations
Module 2 - Python Data Structures
 Lists and Tuples
 Sets
 Dictionaries
Module 3 - Python Programming Fundamentals
 Conditions and Branching
 Loops
 Functions
 Objects and Classes
Module 4 - Working with Files in Python
 Reading files
 Writing files
 Working with and Saving data

2. Object Automation Software Solutions Pvt.
Ltd.
technology
consulting
RECOMMENDED SKILLS
None
TIME TO COMPLETE
30 HOURS
SERVICE AVAILABLE
Technical Support
Installation and Setup
Maintenance
Application Support
Hardware Support
Guaranteed Warranty
For more information or services
please visit us on the Web at:
www.object-a
DATA VISUALIZATION WITH PYTHON
Module 1 - Introduction to Visualization Tools
 Introduction to Data Visualization
 Introduction to Matplotlib
 Basic Plotting with Matplotlib
 Dataset on Immigration to Canada
 Line Plots
Module 2 - Basic Visualization Tools
 Area Plots
 Histograms
 Bar Charts
Module 3 - Specialized Visualization Tools
 Pie Charts
 Box Plots
 Scatter Plots
 Bubble Plots
Module 4 - Advanced Visualization Tools
 Waffle Charts
 Word Clouds
 Seaborn and Regression Plots
RECOMMENDED SKILLS
None
TIME TO COMPLETE
40 HOURS
EARN A CERTIFICATE
When you finish every modules
and complete the performance
evaluation, you’ll earn a
certificate.
DATA ANALYSIS WITH PYTHON
Module 1 - Importing Datasets
 Learning Objectives
 Understanding the Domain
 Understanding the Dataset
 Python package for data science
 Importing and Exporting Data in Python
 Basic Insights from Datasets
Module 2 - Cleaning and Preparing the Data
 Identify and Handle Missing Values
 Data Formatting
 Data Normalization Sets
 Binning
 Indicator variables
Module 3 - Summarizing the Data Frame
 Descriptive Statistics
 Basic of Grouping
 ANOVA
 Correlation
 More on Correlation
Module 4 - Model Development
 Simple and Multiple Linear Regression
 Model Evaluation Using Visualization
 Polynomial Regression and Pipelines
 R-squared and MSE for In-Sample
Evaluation
 Prediction and Decision Making

3. Object Automation Software Solutions Pvt.
Ltd.
SERVICE AVAILABLE
Technical Support
Installation and Setup
Maintenance
Application Support
Hardware Support
Guaranteed Warranty
For more information or services
please visit us on the Web at:
www.object-automation.com
technology
consulting
MACHINE LEARNING TECHNIQUES
Module 1 - Introduction
 Machine Learning Modelling Flow
 How to treat Data in ML
 Parametric & Non-parametric ML Algorithm
 Types of Machine Learning
 Scikit-Learn Library
Module 2 – Linear Regression
 Introduction to Linear Regression
 Linear Regression using Gradient Descent
 Linear Regression using OLS
 Linear Regression using Stochastic
Gradient Descent
Module 3 –Logistic Regression
 Introduction to Logistic Regression
 K Nearest Neighbor
 Understanding KNN
 Choosing K
 Distance Metrics - Euclidean, Manhattan,
Chebyshev
Module 4 –Decision Tree
 Decision Tree
 Fundamental concepts of Ensemble
 Hyper-Parameters
 Bagging - Extra Trees, Random Forest
 Boosting - AdaBoost, Gradient Boosting
Module 5 –SVM
 Vector
 Support Vector Machines (SVM)
 Understanding Hyperplane
 Perceptron Algorithm
 SVM Kernels
 SVM Optimization
 Applications of SVM
Module 5 –Clustering
 What is Clustering?
 K-means Algorithm
 Types of Clustering
 Evaluating K-means Clusters
 Principal Component Analysis (PCA)