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Prerequisites:

  • Basic skills with at least one programming language are desirable. (Optional)

 

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. We offer five unique programs to support your career goals in the data science 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:

o Use Python and SQL to access and analyze data from several different data sources.
o Use principles of statistics and probability to design and execute A/B tests and recommendation
o engines to assist businesses in making data-automated decisions.
o Deploy a data science solution to a basic flask app.
o Manipulate and analyze distributed data sets using Apache Spark.
o Communicate results effectively to stakeholders.

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 ETI 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: Investigate a Dataset
  • Project 6: Analyze Experiment Results
  • Project 7: Wrangle and Analyze Data
  • Project 8: Sentiment Analysis
  • Project 9: Design a Recommendation Engine
  • Project 10: Build AI Chatbot
  • Project 11: Intro to Big Data Project with Spark
  • Project 12: freelance Projects (Kaggle Competitions)

 

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.

 

Program Duration: 130 hours

Program Language: English / Arabic

Location: EPSILON TRAINING INSTITUTE | Head Office

Participants will be granted a completion certificate from Epsilon Training Institute, USA if they attend a minimum of 80 percent of the direct contact hours of the Program and after fulfilling program requirements (passing both Final Exam and Project to obtain the Certificate)

 

DIPLOMA CONTENTS

I- PYTHON 3 TOPICS

  • INTRODUCTION
    • SYNTAX
    • DATA TYPES AND OPERATIONS
    • I/O
    • OPERATORS AND BITWISE
    • LISTS
    • TUPLES
    • IF STATEMENTS
    • FOR – WHILE LOOPS
    • CONTROL FLOW
    • FUNCTIONS
    • SCRIPTING
  • INTRO TO OBJECT-ORIENTED PROGRAMMING (OOP)
    • SPECIAL FUNCTIONS
    • STRINGS
    • CLASSES
    • INHERITANCE
    • REGULAR EXPRESSIONS
    • WORKING WITH FILES
    • PYTHON GENERATORS
    • PYTHON DECORATORS
    • EXCEPTIONS
    • REGULAR EXPRESSIONS
  • INTRO TO DATA SCIENCE
    • DATABASE WITH SQLITE
    • NUMPY AND MATRIX OPERATIONS
    • PANDAS
    • DATA VISUALIZATION
    • GIT COMMAND LINE
    • WEB SCRAPING FOR DATA COLLECTING
  • INTRODUCTION TO VERSION CONTROL
    • SHELL WORKSHOP
    • PURPOSE & TERMINOLOGY
    • CREATE A GIT REPO
    • REVIEW A REPO’S HISTORY
    • ADD COMMITS TO A REPO
    • TAGGING, BRANCHING, AND MERGING
    • UNDOING CHANGES

 

II- DATA STRUCTURES & ALGORITHMS TOPICS

  • INTRODUCTION
    • HOW TO SOLVE PROBLEMS
    • BIG O NOTATION
  • DATA STRUCTURES
    • COLLECTION DATA STRUCTURES (LISTS, ARRAYS, LINKED LISTS, QUEUES, STACK)
    • RECURSION
    • TREES
    • MAPS AND HASHING
  • ALGORITHMS
    • BINARY SEARCH
    • SORTING ALGORITHMS
    • DIVIDE & CONQUER ALGORITHMS
    • MAPS AND HASHING
    • PRACTICE PROBLEMS: RANDOMIZED BINARY SEARCH, K-SMALLEST ELEMENTS USING HEAPS, BUILD RED-BLACK TREE, BUBBLE SORT, MERGE SORT, QUICK SORT, SORTING STRINGS, LINEAR-TIME MEDIAN FINDING

 

III- INTRODUCTION TO SQL TOPICS

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

 

IV- DATA ANALYST TOPICS

  • LINEAR ALGEBRA
  • CALCULUS
  • PRACTICAL STATISTICS
    • SIMPSON’S PARADOX
    • PROBABILITY
    • BINOMIAL DISTRIBUTION
    • CONDITIONAL PROBABILITY
    • BAYES RULE
    • STANDARDIZING
    • SAMPLING DISTRIBUTIONS AND CENTRAL LIMIT THEOREM
    • CONFIDENCE INTERVALS
    • HYPOTHESIS TESTING
    • T-TESTS AND A/B TESTS
    • REGRESSION
    • MULTIPLE LINEAR REGRESSION
    • LOGISTIC REGRESSION
  • INTRODUCTION TO DATA ANALYSIS
    • ANACONDA
    • JUPYTER NOTEBOOKS
    • DATA ANALYSIS PROCESS
    • PANDAS AND NUMPY: CASE STUDY
    • PROGRAMMING WORKFLOW FOR DATA ANALYSIS
    • CLOUD COMPUTING
  • DATA PREPROCESSING
    • IMPORTING LIBRARIES
    • DATA ACQUISITION
    • DATA CLEANING
    • HANDLING MISSING DATA
    • CATEGORICAL DATA
    • DATA SPLITTING
    • FEATURE SCALING
  • REGRESSION PROBLEM
    • LINEAR REGRESSION
    • MULTI-LINEAR REGRESSION
    • POLYNOMIAL REGRESSION
    • K-NEAREST NEIGHBOUR REGRESSION
    • DECISION TREE 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
  • CLUSTERING PROBLEMS
    • DIMENSIONALITY REDUCTION
    • K-MEANS
    • HIERARCHICAL CLUSTERING
  • MODEL SELECTION AND EVALUATION
    • LOSS FUNCTIONS
    • GRADIENT DESCENT
    • BIAS-VARIANCE TRADEOFF
    • CROSS-VALIDATION
    • K Fold
    • HYPERPARAMETER TUNING
  • DATA WRANGLING
    • INTRO TO DATA WRANGLING
    • GATHERING DATA
    • ASSESSING DATA
    • CLEANING DATA
    • POWER BI & EXCEL
  • DATA VISUALIZATION WITH PYTHON
    • DATA VISUALIZATION IN DATA ANALYSIS
    • DESIGN OF VISUALIZATIONS
    • UNIVARIATE EXPLORATION OF DATA
    • BIVARIATE EXPLORATION OF DATA
    • MULTIVARIATE EXPLORATION OF DATA
    • EXPLANATORY VISUALIZATIONS
    • VISUALIZATION CASE STUDY
    • DATA STORYTELLING

V- DATA SCIENTIST TOPICS

  • Solving Problems with Data Science
    • THE DATA SCIENCE PIPELINE
    • COMMUNICATING WITH STAKEHOLDERS
  • SOFTWARE ENGINEERING FOR DATA SCIENTISTS
    • SOFTWARE ENGINEERING PRACTICES
    • OBJECT ORIENTED PROGRAMMING
    • WEB DEVELOPMENT
  • DATA ENGINEERING FOR DATA SCIENTISTS
    • ETL PIPELINES
    • NATURAL LANGUAGE PROCESSING WITH NLTK
    • MACHINE LEARNING PIPELINES
  • EXPERIMENT DESIGN
    • EXPERIMENT DESIGN
    • STATISTICAL CONCERNS OF EXPERIMENTATION
    • A/B TESTING
  • RECOMMENDATIONS
    • INTRODUCTION TO RECOMMENDATION ENGINES AND TYPES
    • MATRIX FACTORIZATION FOR RECOMMENDATIONS
  • INTRODUCTION TO BIG DATA WITH SPARK AND INTEGRATION WITH ML
  • CAPSTONE PROJECT

 

 

Download Data Scientist Brochure PDF

DATA_SCIENTIST Ver#4

Course Curriculum

PYTHON 3 TOPICS
INTRODUCTION
OBJECT-ORIENTED PROGRAMMING (OOP)
WEB APP
INTRO TO DATA SCIENCE
INTRODUCTION TO VERSION CONTROL
DATA STRUCTURES & ALGORITHMS TOPICS
INTRODUCTION
DATA STRUCTURES
BASIC ALGORITHMS
ADVANCED ALGORITHMS
DATA ANALYST TOPICS
LINEAR ALGEBRA
CALCULUS
PRACTICAL STATISTICS
INTRODUCTION TO DATA ANALYSIS
DATA PREPROCESSING
REGRESSION PROBLEM
CLASSIFICATION PROBLEM
CLUSTERING PROBLEMS
REINFORCEMENT LEARNING
MODEL SELECTION AND EVALUATION
DATA WRANGLING
DATA VISUALIZATION WITH PYTHON
DATA SCIENTIST TOPICS
Solving Problems with Data Science
SOFTWARE ENGINEERING FOR DATA SCIENTISTS
DATA ENGINEERING FOR DATA SCIENTISTS
EXPERIMENT DESIGN
RECOMMENDATIONS
INTRODUCTION TO BIG DATA WITH SPARK AND INTEGRATION WITH ML
Final Project

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