# Certificate in Data Science

#### Description

The Certificate in Data Science is an integrated course with SAS programming essentials, data step manipulation, Predictive modelling, SQL, R and Python. This program is most suitable for job seekers who want to become a Data Scientist on SAS, R and Python to manipulate data, perform queries and analyses, and generate reports, with predictive modeling techniques. This program will cover the curriculum for SAS Global certifications.

#### What Will I Learn?

- Hands-On Capstone project
- Case studies
- For the industry by the industry
- Cutting edge curriculum

#### Topics for this course

#### Lesson 1: An Overview of the SAS System

#### Lesson 2: Getting Started With the SASÂ® System

#### Lesson 3: Getting familiar with the SAS Data Sets

#### Lesson 4: Producing List Report

#### Lesson 5: Enhancing Output

#### Lesson 6: Creating SASÂ® Data Sets

#### Lesson 7: Data Step Programming

#### Lesson 8: Combining SAS Data Sets

#### Lesson 9: Producing Summary Reports

#### Lesson 10: Introduction to Graphics

#### Lesson 11: Introduction to Data Step Manipulation

#### Lesson 12: Controlling Input and Output

#### Lesson 13: Summarizing Data

#### Lesson 14: Reading & Writing Different Types of Data

#### Lesson 15: Data Transformations

#### Lesson 16: Debugging Techniques

#### Lesson 17: Processing Data Iteratively

#### Lesson 18: Combining SASÂ® Data Sets

#### Lesson 19: Introduction to the SQL Procedure

#### Lesson 20: Retrieving Data From a Single Table

#### Lesson 21: Retrieving Data from Multiple Tables

#### Lesson 22: Creating and Updating Tables and Views

#### Lesson 23: Programming with the SQL Procedure

#### Lesson 24: Practical Problem-Solving with PROC SQL

#### Lesson 25: SAS MACROS

#### Lesson 26: Introduction to Analytics and Basic Statistics

#### Lesson 27: Introduction to Probability Theory

#### Lesson 28: Sampling Theory And Estimation

#### Lesson 29: Theory of Estimation

#### Lesson 30: Testing of hypothesis

#### Lesson 31: Analysis of Variance

#### Lesson 32: Exploratory Factor Analysis

#### Lesson 33: Cluster Analysis

#### Lesson 34: Linear Regression and Multiple Linear Regression

#### Lesson 35: Logistic Regression

#### Lesson 36: Time Series Analysis

#### Lesson 37: Introduction to R programming

#### Lesson 38: R programming essentials

#### Lesson 39: Fundamentals of R Language

#### Lesson 40: Data Visualization and Data Manipulation

#### Lesson 41:Testing of Hypothesis

#### Lesson 42: Exploratory Factor Analysis

#### Lesson 43: Machine Learning with R-language

#### Lesson 44: Analysis of Variance

#### Lesson 45: Linear Regression Using R

#### Lesson 46: Logistic Regression Using R

#### Lesson 47: Time Series Analysis

#### Lesson 48: Decision tree and Clustering

#### Lesson 49: Text Mining and Sentiment Analysis

#### Lesson 50: Market Basket Analysis

#### Lesson 51: Data Science Fundamentals

#### Lesson 52: Data Science Implementation

#### Lesson 53: The Impact of Data Science

#### Lesson 54: Introduction to Analytics and Statistics

#### Lesson 55: Introduction to Python Programming

#### Lesson 56: Fundamentals of Python Programming

#### Lesson 57: Python Data Types

#### Lesson 58: Python OOPs concept and Function

#### Lesson 59: Python File Handling

#### Lesson 60: Data Analysis with Python and Visualization

#### Lesson 61: Introduction to Machine Learning

#### Lesson 62: Linear Regression Analysis

#### Lesson 63: Logistic Regression Analysis

#### Lesson 64: Other Classification Algorithms

#### Lesson 65: Dimensionality Reduction

#### Lesson 66: Clustering Techniques

#### Lesson 67: Introduction to Neural Network

#### Lesson 68: Software and Libraries for Neural Network

#### Lesson 69: Artificial Neural Network (ANN)

#### Lesson 70: ANN Implementation

#### Lesson 71: Convolution Neural Network (CNN)

#### Lesson 72: Recurrent Neural Network (RNN)

I got a very Descriptive picture of Data science from this Course. All the topics were very well explained in this Course.

Handson was recommended by one of my friend. Every topic was taught in a very detailed manner. Highly Recommendable.

The course content is too good. I have completed SAS and R and pursuing Python at present. The instructor is very good and taught very interestingly. The course turned out to be very useful for me.

I am a beginner in Data Science, with no technical knowledge. The instructor in this course have simplified the contents so well that made me understand very easily. Every topic was taught in a very systematic manner with practical examples.

As a non-technical professional it was a great experience learning from the basics and to the end. I learnt from hands-on sessions and they are very friendly. I am very happy to attend this program offered by Handson.

Thanks a lot to my faculty, he guided me throughout the project work. Handson organizes training programs to avail students of professional learning in the field of analytics and data science.

I have completed R. Now I am doing my project work. Really a good experience.

One of my friends recommended me for Handson. It was a great learning experience with Handson, I enrolled in the Certificate in Data Science course. Three programming languages SAS, R, and Python was taught from the beginner's level to advanced level. Faculty has good knowledge and was very supportive.