Advanced Data Science is the aspect of data engineering that focuses on practical applications of data collection and analysis.

Duration: (12 weeks)

Intakes: Jan,April,July,Oct

Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In order for that work to ultimately have any value, there also have to be mechanisms for applying it to real-world operations in some way. Those are both engineering tasks: the application of science to practical, functioning systems. Data engineers focus on the applications and harvesting of big data. Data engineers are often responsible for building algorithms to help give easier access to raw data, but to do this, they need to understand company’s or client’s objectives.

Course Topics for Data science Prep (12 Weeks)

The 12 Weeks Data science Program will be offering the below for both entry, intermediate and expert level.In this class you will learn:

Data Science (Duration: 12 Weeks):

Week	Topic	What to Learn
Week 1	1. Careers In DS and AI (Theory) 2. The CRISP-DM methodology (Theory) 3. Introduction to Python basics for data science 4. SQL Basics for Data Science	1. To load datasets 2. Data exploration 3. To perform Filtering and Sorting 4. To use aggregate functions 5. To use pivot tables
Week 2	1. Data Wrangling with python 2. Ethics for Data Science (Theory)	1. Checking for missing values 2. Embedding missing values 3. Checking for outliers 4. Dataset merging
Week 3	1. Data Visualization with python Reading 2. Data Visualization with python practice 3. Advanced Data Visualization	1. Matplotlib 2. Seaborn 3. Plotly 4. PowerBI Overview 5. Tableau Overview
Week 4	ADS Platform, GitHub, and git overview	1. Practicals
Week 5	1. Data Analysis and Reporting with SQL (Reading and Practice)	1. SQL analysis techniques
Week 6	1. Data Analysis and Reporting with Python (Reading and Practice)	1. Python analysis techniques
Week 7	1. Experimental Research Design (Reading) 2. Machine Learning models (Reading and Practice)	1. Regression analysis with python
Week 8	1. Machine Learning models (Reading and Practice)	1. Classification analysis with Python
Week 9	1. Ensemble Learning with python	1. Ensemble Learning models
Week 10	1. Sampling Techniques with Python 2. Clustering Analysis with python	1. Clustering analysis Techniques 2. Sampling Techniques

Week 11

1. Feature Engineering with python (Reading and Practice)

2. Hyperparameter tuning (Reading and Practice)

1. Creative thinking

2. Feature Engineering techniques

3. Hyperparameter tuning techniques

Week 12

Introduction to Deep Learning and Artificial Intelligence

Capstone Project

1. Preparation

2. Guidance

3. Submission

Benard Mariten

A Data Scientist who has considerable expertise and superior knowledge in the subject. With over 4 years of expertise in big data and analytics, and he has aided many businesses in achieving both their short- and long-term objectives.

PROFESSIONAL SKILLS

Data Visualization
Data Analysis
Natural Language Processing
Predictive Modeling
Python

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