Course Outline for Certified Data Science Developer

Course Outline for Certified Data Science Developer

Certified Data Science Developer™ Certification training focuses on analyzing and testing the core concepts of data sciences, right from the elementary concepts such as statistics, data management and analytics to advanced topics like neural networks and machine learning and R programming. As certifications from Global Tech council are recognized and valued worldwide, Data Science Developer Certification aids to your credibility for the various job roles in Data science domain.

As demand for data science developer soars, individuals can grasp this excellent opportunity to get certified in data sciences. Certified Data Science Developer™ certification aims to provide individuals with a competitive edge for superior employment opportunities.

Course Outcome

By the of the course, learner may be expected to grasp and utilize:

• Python for Data Science
• R programming
• Data Preprocessing
• Data Visualization
• Introduction to Machine Learning
• Supervised Learning - Regression
• Supervised Learning - Classification
• Unsupervised Learning - Clustering
• Dimensionality Reduction
• Recommendation Engine
• Association Rules
• Time Series
• Statistics

Certification

• There will be an online examination of multiple choice exam of 100 marks.
• You need to acquire 60+ marks to clear the exam.
• If you fail, you can retake the exam after one day.
• You can take the exam no more than 3 times.

Prerequisites

In order to be able to participate in this course, the learner needs to comply with the following conditions:

• Access to PC [either one of these OS: MS Windows / Mac / Linux (ubuntu)]
• Full admin / root access to the above mentioned PC
• Experience and understanding of filing system
• Internet connection
• Webcam
• Microphone
• Dual screens
• Very basic understanding of server architecture
• Ability to comprehend the English language (as the examination is in English)
• Minimum 99% attendance
• Basic education in mathematics (understanding of linear and non-linear mathematics at high-school level)
• Google account

Course Outline

This is a 7 day course that will cover the following topics:

1. Basics of Python for Data Science
   1. How to Install Python
   2. History of Python
   3. Python Variables
   4. Loops in Python
   5. Python collection Data Types
   6. OOPS concepts
   7. Exception Handling
   8. Regular Expression
   9. Python Numpy Arrays
   10. Matrix and its operation
   11. Functions in Python
   12. User Defined functions in Python
   13. Scope in Python
   14. Introduction to Methods
   15. Packages in Python and PIP
   16. Pandas and Data frames
   17. Import and Export data from CSV
2. Data Preprocessing
   1. Why Data Preprocessing
   2. Missing Values Treatment
   3. Encoding
   4. Feature Scaling
   5. Outlier Treatment
   6. Template for Data Preprocessing
3. Data Visualization
   1. Introduction to Matplotlib and Seaborn
   2. Various charts and syntax
4. Introduction to Machine Learning
   1. Introduction to Machine Learning
   2. Types of Machine Learning
   3. Basic Probability required for Machine Learning
   4. Linear Algebra required for Machine Learning
5. Supervised Learning - Regression
   1. Simple Linear Regression
      1. Simple Linear Regression Intuition
      2. Simple Linear Regression – Business Problems
   2. Multiple Linear Regression
      1. Multiple Linear Regression Intuition
      2. Multiple Linear Regression – Business Problems
   3. Polynomial Regression
      1. Polynomial Regression Intuition
      2. Polynomial Regression Business problem
      3. Implementation
   4. Decision Tree
      1. Decision Tree Intuition
      2. Decision Tree Business problem
      3. Implementation
   5. Random Forest
      1. Random Forest Intuition
      2. Random Forest Business problem
      3. Implementation
6. Supervised Learning - Classification
   1. Logistic Regression
   2. Decision Trees
   3. Random Forests
   4. SVM
   5. Naïve Bayes
   6. KNN
   7. Confusion Matrix
7. Unsupervised Learning - Clustering
   1. Types of K-Means
   2. K-Means Clustering
   3. Hierarchal Clustering
8. Dimensionality Reduction
   1. Principal Component Analysis
   2. Linear Discriminant Analysis
9. Recommendation Engine
   1. Need for recommendation engines
   2. Types of Recommendation Engines
   3. Content-Based
   4. Collaborative Filtering
10. Association Rules
    1. Apriori Algorithm
    2. Market Basket Analysis
11. Time Series
    1. Understanding Time Series Data
    2. ARIMA analysis
12. Statistics
    1. Statistics – Descriptive Statistics
    2. Statistics – Inferential Statistics Fundamentals
    3. Statistics – Hypothesis Testing
13. Summary
    1. Summary of the key learning of the course.
14. Practice test