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Prerequisites:

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

 

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.
  • Become a Data Engineer Data Engineering is the foundation for the new world of Big Data. Enroll now to build production-ready data infrastructure, an essential skill for advancing your data career.
  • Objectives: Trainees will learn to
    o Create user-friendly relational and NoSQL data models
    o Create scalable and efficient data warehouses
    o Identify the appropriate use cases for different big data technologies
    o Work efficiently with massive datasets
    o Build and interact with a cloud-based data lake
    o Automate and monitor data pipelines
    o Develop proficiency in Spark, Airflow, and AWS tools

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: Finding Donors for Charity
      • Project 4: Creating Customer Segments Deep learning
      • project 5: Dog Breed Recognition
      • Project 6: Teach a Quad copter to Fly
      • Project 7: Explore Weather Trends
      • Project 8: Investigate a Dataset
      • Project 9: Analyze Experiment Results
      • Project 10: Wrangle and Analyze Data
      • Project 11: Communicate Data Findings
      • Project 12: Data Modeling with Postgres and Apache Cassandra
      • Project 13: Data Infrastructure on the Cloud
      • Project 14: Big Data with Spark
      • Project 15: Automate Data Pipelines
      • Project 16: Final Project
    • Capstone projects in many fields
      • Business
      • Trading

 

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 analytical techniques and models.
• demonstrate the ability to incorporate various data analytics elements.
• demonstrate an understanding of fundamental principles of data analytics systems and technologies.

 

Program Duration: 150 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)

 

COURSE CONTENTS

 

I- INTRODUCTION TO SQL TOPICS

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

 

 

II- 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
  • OBJECT-ORIENTED PROGRAMMING (OOP)
    • SPECIAL FUNCTIONS
    • STRINGS
    • CLASSES
    • INHERITANCE
    • REGULAR EXPRESSIONS
    • WORKING WITH FILES
    • PYTHON GENERATORS
    • PYTHON DECORATORS
    • EXCEPTIONS
    • REGULAR EXPRESSIONS
    • MULTITHREADING AND MULTIPROCESSING SOCKETS AND APIS
  • INTRODUCTION TO GUI
    • GUI GRID
    • GUI EVENTS
    • GUI STYLES
  • INTRO TO DATA SCIENCE
    • DATABASE WITH SQLITE
    • NUMPY AND MATRIX OPERATIONS
    • PANDAS
    • MATPLOTLIB
    • BUILDING YOUR OWN SERVER
    • DATA VISUALIZATION
    • GIT COMMAND LINE AND GUI BASED
    • 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

 

III- 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
  • BASIC 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
  • ADVANCED ALGORITHMS
    • GREEDY ALGORITHMS
    • GRAPH ALGORITHMS
    • DYNAMIC PROGRAMMING
    • LINEAR PROGRAMMING
    • PRACTICE PROBLEMS: GRAPH TRAVERSALS, DIIJKSTRA’S ALGORITHM, SHORTEST HOPS, A* SEARCH, LONGEST PALINDROMIC SUBSEQUENCE, WEB CRAWLER

 

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 1
    • PANDAS AND NUMPY: CASE STUDY 2
    • PROGRAMMING WORKFLOW FOR DATA ANALYSIS
  • 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
    • DBSCAN
    • HIERARCHICAL CLUSTERING
    • ASSOCIATION RULES
  • REINFORCEMENT LEARNING
    • UPPER CONFIDENCE BOND
    • THOMPSON SAMPLING
  • MODEL SELECTION AND EVALUATION
    • LOSS FUNCTIONS
    • GRADIENT DESCENT
    • BIAS-VARIANCE TRADEOFF
    • CROSS-VALIDATION
    • HYPERPARAMETER TUNING
  • DATA WRANGLING
    • INTRO TO DATA WRANGLING
    • GATHERING DATA
    • ASSESSING DATA
    • CLEANING DATA
  • 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

V- DATA ENGINEER TOPICS

  • Data Modeling
    • Introduction to Data Modeling
    • Relational Data Models
    • NoSQL Data Models
  • Cloud Data Warehouses
    • Introduction to the Data Warehouses
    • Introduction to the Cloud with AWS
    • Implementing Data Warehouses on AWS
  • Data Lakes with Spark
    • The Power of Spark
    • Data Wrangling with Spark
    • Debugging and Optimization
    • Introduction to Data Lakes
  • Automate Data Pipelines
    • Data Pipelines
    • Data Quality
    • Production Data Pipelines
  • FINAL PROJECT

 

 

Download Data Engineer Brochure PDF

DATA_ENGINEER

 

Course Curriculum

INTRODUCTION TO SQL TOPICS
PYTHON 3 TOPICS
INTRODUCTION
OBJECT-ORIENTED PROGRAMMING (OOP)
INTRODUCTION TO GUI
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 ENGINEER TOPICS
Data Modeling
Cloud Data Warehouses
Data Lakes with Spark
Automate Data Pipelines

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