here is our content of the complete course in Data Science # join us now
• Introduction to DataBases
Database Management System (DBMS)
DBMS Architecture.
Entity Relationship Modeling.
Relational Database.
• SQL fundamentals
Data Definition Language (DDL)
Data Manipulation Language (DML)
Data Control Language (DCL)
• Data Warehouses concept (inmon and Kimball)
Understand today’s business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting opportunities)
Describe the business intelligence (BI) architecture component.
Data Warehousing.
Star vs Snowflake schema.
Analysis of Data in DW (OLAP and OLTP).
• A Brief look at ELT and Reporting concepts
What is ETL and what are the main integration concepts.
Introduction to informatica.
Mapping and Transformations.
Tasks and Workflow.
Reporting concepts (Analytical Queries).
• Business Statistics
Introduction.
Describing Data Sets “Graphs, Charts, and Tables”.
Describing Data Using Numerical Measures.
Using probability and probability Distributions.
Estimating Population Values.
Introduction to Hypothesis Testing.
Analysis of Variance.
Goodness-of-fit Test and Contingency Analysis.
Introduction to Linear Regression and Correlation Analysis.
Time Series Analysis.
• Data Mining fundamentals and techniques
Introduction to Machine Learning.
Decision Trees.
Linear Vs Logistic Regression.
• Introduction to Python as an analytics tool
Introduction to Python.
Python Variables and data types.
Python Functions and methods.
• Applied Data Science and analytics through Python
Main data science package and their usage.
Numpy and Pandas.
Data Preparation with Pandas.
Machine learning with scikit learn.
• Visualization techniques and communicating the results
Exploratory analysis and statistical thinking.
Basics data visualization.
Plotting charts using Matplotlib Package in Python and interpreting the Results.
• Project on real datasets from kaggle
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