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Course Overview

Unlock your data science career with Aptech’s specialized Data Science course in Dubai and Sharjah. Dive into methods for managing and analyzing large datasets, study Python, Excel, BI, cloud technologies, and big data. Become a skilled data scientist!

Tools To Master

  • Microsoft Excel
  • Python
  • SQL
  • Power BI
  • Jupyter
  • SciPy
  • NumPy
  • pandas
  • Matplotlib

Skills To Master

Python, Data Science, Data Analysis, Data Wrangling, SQL, Machine Learning, Prediction Algorithms, Data Visualization


Module 1 – Preparatory Session - Python

Python 

Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 

Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling etc.

Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.


Module 2 – Data Analysis With Ms-Excel

Excel Fundamentals 

Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 

Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 

IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

Data Visualization with Excel

Charts, Pie charts, Scatter and bubble charts

Bar charts, Column charts, Line charts, Maps

Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Classification Problems using Excel

Binary Classification Problems, Confusion Matrix, AUC and ROC curve 

Multiple Classification Problems  

Information Measure in Excel

Probability, Entropy, Dependence 

Mutual Information 

Regression Problems Using Excel

Standardization, Normalization, Probability Distributions 

Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation

Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression



Module 3 – Data Wrangling With Sql

SQL Basics – 

Fundamentals of Structured Query Language

SQL Tables, Joins, Variables 

Advanced SQL –  

SQL Functions, Subqueries, Rules, Views

Nested Queries, string functions, pattern matching

Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

Types of UDFs, Inline table value, multi-statement table. 

Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

Record grouping, searching, sorting, etc. 

Clustered indexes, common table expressions.


Module 4 – Python With Data Science

Extract Transform Load

Web Scraping, Interacting with APIs

Data Handling with NumPy

NumPy Arrays, CRUD Operations, etc.

Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

Loading the data, dataframes, series, CRUD operations, splitting the data, etc.

Data Preprocessing

Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.

Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.

Data Visualization

Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.

Regression plots, categorical plots, area plots, etc, with Python seaborn.


Module 5 – Linear Algebra And Advanced Statistics

Descriptive Statistics – 

Measure of central tendency, measure of spread, five points summary, etc. 

Probability 

Probability Distributions, bayes theorem, central limit theorem. 

Inferential Statistics –  

Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.



Module 6 – Machine Learning

Introduction to Machine learning 

Supervised, Unsupervised learning.

Introduction to scikit-learn, Keras, etc.

Regression 

Introduction classification problems, Identification of a regression problem, dependent and independent variables.

How to train the model in a regression problem.

How to evaluate the model for a regression problem.

How to optimize the efficiency of the regression model.

Classification 

Introduction to classification problems, Identification of a classification problem, dependent and independent variables.

How to train the model in a classification problem.

How to evaluate the model for a classification problem.

How to optimize the efficiency of the classification model.

Clustering 

Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.

How to train the model in a clustering problem.

How to evaluate the model for a clustering problem.

How to optimize the efficiency of the clustering model.

Supervised Learning 

Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.

Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.

Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.


Module 7 – Power Bi

Power BI Basics

Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.

Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features





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