Data Science Specialist Certificate

1-Learning Methodology

• Instructor-Led Classroom Training (ILT).

2-Prerequisites:

• Basic skills with at least one programming language are desirable
• Familiar with the basic math and statistic concepts

3-Training Program Description:

• Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science. With the skills you learn in a program, you can launch or advance a successful data career. Start acquiring valuable skills right away, create a project portfolio to demonstrate your abilities, and get support from mentors, peers, and experts in the field.
• Gain real-world data science experience with projects designed by industry experts. Build your portfolio and advance your data science career.

The ultimate goal of the Data Scientist program is for you to learn the skills you need to perform well as a data scientist. As a graduate of this program, you will be able to:

• Use Python and SQL to access and analyze data from several different data sources.
• Use principles of statistics and probability to design and execute A/B tests and recommendation
• engines to assist businesses in making data-automated decisions.
• Communicate results effectively to stakeholders.

Length of Program: 11 Weeks

4-program outcomes:

• Use Python and SQL to access and analyze data from several different data sources.
• Build predictive models using a variety of unsupervised and supervised machine learning techniques.
• Perform feature engineering to improve the performance of machine learning models.
• Optimize, tune, and improve algorithms according to specific metrics like accuracy and speed.
• Compare the performances of learned models using suitable metrics.
• demonstrate critical thinking skills in the field of data analytics.
• ability to solve problems related to the program content.
• analyze, design and document a system component using appropriate data analysis techniques and models.
• demonstrate the ability to incorporate various data analytics elements.
• demonstrate an understanding of the fundamental principles of data analytics systems and technologies.

5-Projects

•  This program is comprised of many career-oriented projects. Each project you build will be an opportunity to demonstrate what you’ve learned in the lessons. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in data analysis and feature engineering, machine learning algorithms, and training and evaluating models.
• One of our main goals at EAII is to help you create a job-ready portfolio of completed projects. Building a project is one of the best ways to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers or colleagues. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects
• Building a project is one of the best ways both to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects:

• Project 1: Exploring the Titanic Survival Data
• Project 2: Predicting Housing Prices
• Project 3: Creating Customer Segments
• Project 4: Credit Card Fraud Detection
• Project 5: Design a Recommendation Engine
• Project 6: Intro to Big Data Project with Spark
• Project 7: freelance Projects (Kaggle Competitions)
• Capstone Project

6-Training Program Curriculum:

I- PYTHON 3 TOPICS

• INTRODUCTION
  • SETTING UP PYTHON
  • SYNTAX
  • FIRST PYTHON PROGRAM
  • VARIABLES & DATA TYPES
  • NUMBERS AND MATH
  • STRINGS
  • OPERATORS AND BITWISE
  • DATA STRUCTURES OF PYTHON
    • TUPLES
    • LISTS
    • SETS
    • DICTIONARIES
  • CONDITIONAL LOGIC AND CONTROL FLOW
  • FUNCTIONS AND LOOPS
• OBJECT-ORIENTED PROGRAMMING (OOP)
  • CLASSES
  • INHERITANCE
  • PYTHON GENERATORS
  • PYTHON DECORATORS

II-SQL TOPICS

• • BASIC SQL
  • SQL JOINS
  • SQL AGGREGATIONS
  • SQL QUERIES

III-DATA ANALYSIS TOPICS

• DATA ANALYSIS
  • ANACONDA / JUPYTER NOTEBOOKS
  • DATABASE WITH SQLITE
  • NUMPY AND MATRIX OPERATIONS
  • PANDAS
  • DATA VISUALIZATION
• DATA WRANGLING
  • INTRO TO DATA WRANGLING
  • GATHERING DATA
  • ASSESSING DATA
  • CLEANING DATA
  • POWER BI & EXCEL

IV-MACHINE LEARNING TOPICS

• LINEAR ALGEBRA
  • VECTORS
  • MATRICES
  • OPERATIONS ON MATRICES
  • DOT PRODUCT
  • EIGEN VALUES AND EIGEN VECTORS
• CALCULUS
  • FUNCTIONS
  • DERIVATIVES AND GRADIENTS
  • STEP FUNCTION, SIGMOID FUNCTION, RELU
  • COST FUNCTION
  • MINIMUM AND MAXIMUM VALUES
• STATISTICS AND PROBABILITY
  • DESCRIPTIVE STATISTICS
    • INTRODUCTION
    • SAMPLING TECHNIQUES
    • MEASURES OF CENTRAL TENDENCY
    • MEASURES OF VARIABILITY
    • SKEWNESS AND OUTLIERS
  • INFERENTIAL STATISTICS
    • T-TEST AND ANOVA
    • CHI-SQUARE TEST
    • SPEARMAN CORRELATION COEFFICIENT
    • PEARSON CORRELATION COEFFICIENT
    • REGRESSION ANALYSIS
  • PROBABILITY
    • PROBABILITY LAWS
    • BAYESIAN THEOREM
    • PROBABILITY DISTRIBUTION
    • GAUSSIAN DISTRIBUTION
    • SAMPLING DISTRIBUTION
    • CENTRAL LIMIT THEOREM
  • INTRODUCTION TO ML AND BUSINESS CASES
    • THE DIFFERENCE BETWEEN ML, BIG DATA, DATA ANALYSIS AND DEEP LEARNING
  • DATA PREPROCESSING
    • IMPORTING LIBRARIES
    • DATA ACQUISITION
    • DATA CLEANING
    • HANDLING MISSING DATA
    • CATEGORICAL DATA
    • DATA SPLITTING
    • FEATURE SCALING

• REGRESSION PROBLEM
  • LINEAR REGRESSION
  • POLYNOMIAL REGRESSION
  • REGRESSION EVALUATION METRICS
• CLASSIFICATION PROBLEM
  • LOGISTIC REGRESSION
  • NAIVE BAYES
  • K-NEAREST NEIGHBOUR CLASSIFIER
  • SUPPORT VECTOR MACHINE (SVM)
  • DECISION TREE CLASSIFIER
  • ENSEMBLE LEARNING
  • CLASSIFICATION EVALUATION METRICS
• INTRO TO BUILDING MACHINE LEARNING API
• CLUSTERING PROBLEMS
  • DIMENSIONALITY REDUCTION
  • K-MEANS
  • HIERARCHICAL CLUSTERING
  • ASSOCIATION RULES
  • CLUSTERING EVALUATION
• MODEL SELECTION AND EVALUATION
  • CROSS-VALIDATION
  • HYPERPARAMETER TUNING
• RESULT COMMUNICATION AND REPORT
• INTRODUCTION TO DEEP LEARNING / NLP / COMPUTER VISION
  • INTRO TO DEEP LEARNING WITH KERAS
    • INTRO TO ANN – ARTIFICIAL NEURAL NETWORK
    • INTRO TO CNN _CONVOLUTION NEURAL NETWORK
    • INTRO TO RNN_RECURRENT NEURAL NETWORK
    • INTRO TO AUTOENCODER
  • INTRO TO REINFORCEMENT LEARNING / INVERSE REINFORCEMENT LEARNING
  • INTRO TO NLP
  • INTRO TO COMPUTER VISION

V-DATA SCIENTIST TOPICS

• SOLVING PROBLEMS WITH DATA SCIENCE
  • THE DATA SCIENCE PIPELINE
  • COMMUNICATING WITH STAKEHOLDERS
• DATA ENGINEERING FOR DATA SCIENTISTS
  • ETL PIPELINES
  • MACHINE LEARNING PIPELINES
• EXPERIMENT DESIGN
  • EXPERIMENT DESIGN
  • A/B TESTING
• DATA LAKE VS DATA WAREHOUSE
• INTRODUCTION TO BIG DATA WITH SPARK AND INTEGRATION WITH ML