Data Science & Python Track
Abstract • Build essential statistical knowledge and how to apply it on different business questions. • Will be able to analyze your data quickly and visualize your results using SPSS and Python. • You will learn how to use Python to analyze and visualize your data using different libraries. • You’ll explore the four crucial […]
Abstract
• Build essential statistical knowledge and how to apply it on different business questions.
• Will be able to analyze your data quickly and visualize your results using SPSS and Python.
• You will learn how to use Python to analyze and visualize your data using different libraries.
• You’ll explore the four crucial steps for any data analysis project. Reading, describing, cleaning, and visualizing data.
• You will work with the most common and popular tools that data analysts use every day.
• You will be able to confidently extract knowledge and answers from data.
• Build your expertise in the most widely used AI & ML tools and technologies.
• Understand the complete Data Science life cycle and its application on different Data Sets
• Acquire the ability as a data scientist to independently solve business problems using machine learning.
• Learn how data scientists exactly work in completing an end-to-end project. Starting from the understanding of a business problem then, data visualization, data preparation and moving through the whole data pipeline till deployment and reporting the results in the most effective way.
• Apply the whole data science life cycle on different real world use cases (end to end)
Instructor
Prof. Hazem Shatila, Virginia Tech University, USA
Eng. Ahmed Yehia, Markov
Eng.Engi Amin, Markov
Dr|sahar
Amr helal
Duration
4 Months
Sessions
Sunday & Tuesday 7.00PM -10.00pm
Location
Live Streaming (on-line)
Prerequisites
None
1- Introduction to Python (30hrs)
Part 1: Introduction to Programming
1. Thinking like a programmer
2. Representing problems and solutions using algorithms, flowcharts, or
pseudocode
1. Low-level languages
2. High-level languages
1. Interpreter vs compiler
2. Input, output, sequential execution, conditional execution, repeated execution, reuse
3. Types of errors
4. Debugging
Part 2: Python Basics
1. Variables and variable types
2. Python statements (print & assignment)
3. Python operators (arithmetic & string)
4. Python expressions
5. Asking the user for input
6. Notes on python syntax and commenting
1. Boolean expressions
2. Logical operators
3. Conditional executing using IF statement
4. Alternative execution
5. Chained conditionals
6. Nested conditionals
1. Built-in functions
2. User-defined functions
3. Variable scope
1. What is an iteration or a loop?
2. Finite loops
• Definite (For loops)
• Indefinite (While loops)
3. Infinite loops
1. List properties
2. Traversing a list
3. List operations
4. List methods
5. Deleting items
6. Lists and functions
1. String properties
2. Traversal through string
3. String methods
1. Dictionary/Tuples properties
2. Creating Dictionaries/tuples
3. Accessing items in a dictionary/tuple
1. Opening files
2. Reading files
3. Searching through a file
4. Letting the user choose file name
5. Writing files
Part 3: Exploratory Data Analysis using Python
1. NumPy
2. Pandas
3. Matplotlib
4. Seaborn
1. Arrays
2. Numpy universal functions
1. Pandas data structures
2. Indexing, selection, and filtering
3. Summarizing and computing descriptive statistics
1. Assessing Data
• Quality issues
• Structural issues
2. Data Cleaning and Tidying
• Sub-setting data
• Merging datasets
• Joining datasets
• Reshaping data
1. Data aggregation and group operations
2. Pivot tables and cross tabulations
3. Plotting with matplotlib/seaborn
2- Data Analysis & Visualization Using Python (30hrs)
Module 1: introduction to basics of Statistics.
Module 2: Data preparation and Descriptive Statistics using SPSS-software
Module 3: Data Analysis & Visualization using Python Introduction to Python.
Data Visualization
Data Prediction Models
Use Case-1
Use Case-2
Use Case-3
Use Case-4
3- Data Science and Python (36hrs)
Module 1: Statistics
Linear Algebra & Probability for Data Science
1- Measures of Central Tendency
2- Measures of Variability
3- Skewness and Outliers
1. Introduction to Probability
2. Probability Laws
3. Bayesian Theorem
4. Probability Distribution
5. Gaussian Distribution
6. Sampling Distribution
7. Central Limit Theorem
1. Z-score
2. Min-Max Method
3. Decimal Scaling Method
1. T-Test and ANOVA
2. Chi-Square Test
3. Spearman Correlation Coefficient
4. Pearson Correlation Coefficient
5. Regression Analysis
1. Review on Matrices
2. Operations on Matrices
3. Eigen Values and Eigen Vectors.
4. Dimensionality Reduction (Principal Component Analysis - PCA)
Module 2: Data Science and Machine Learning
1. Linear Regression
2. Polynomial Regression
1. Logistic Regression
2. Decision Tree based Methods
3. K-Nearest Neighbor
4. Neural Networks
5. Naïve Bayes
6. Support Vector Machines
1. K-mean Clustering
2. Hierarchical Clustering
3. Cluster Evaluation
1. F1-Score
2. ROC
3. Lift Curves
Module 3: Python for Data Science & Machine Learning
1. General Syntax
2. Data Types
1. Lists
2. Tuples
3. Sets
4. Dictionaries
1. Functions
2. Methods
3. Loops
4. Conditional Statements
5. Classes and Objects
1. Numpy
2. Pandas
3. Matplotlib
4. Seaborn
5. Sklearn
Module 4: Class Projects
1. Importing Packages
2. Loading the Data
3. Data Preprocessing
4. Build Frequent Item Set
5. Crating Association Rules
1. Importing packages
2. Loading the data
3. Date Preprocessing
4. Creating Linear Model
5. Evaluating the Model
1. Importing Packages
2. Loading the Data
3. Data Exploration
4. Date Preprocessing
5. Split the Data (train & Test)
6. Train Logistic Regression Algorithm
7. Test the Trained Model
8. Evaluating the Model
1. Importing Packages
2. Loading the Data
3. Date Preprocessing
4. Split the Data (Train & Test)
5. Train NN Algorithm.
6. Test the trained Model
7. Evaluating the Model
1. Importing Packages
2. Loading the Data.
3. Date Preprocessing
4. Choose the Optimum Number of Clusters
5. Apply K-Means
6. Visualize the Output
