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
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.
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 |
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.
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.
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 |
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.
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