Launch your Data Analytics career

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Overview

Become a Data Analyst

Discover the process of examining and evaluating raw data in order to bring meaning and insight that empowers intelligent decision-making, leading you to rewarding careers as:

  • Data Analyst
  • Operations Analyst
  • Data Engineer
  • Learn how to turn raw data into meaningful insights
  • Explore beginner and intermediate statistics
  • Inclusive of coding basics for more understanding of data manipulation and analysis techniques
  • Engage in hands-on exercises that replicate the challenges faced by data professionals in various industries

You’ll learn the tools and languages analysts use, like:

Microsoft Excel
Python
Tableau
PostgreSQL
Pandas
dplyr
NumPy
ggplot2
Matplotib
Jupyter
MongoDB
tidyverse
Linux
Githhub
Git
R
DBeaver
Learning Schedule

Learning schedules that align with your lifestyle and career goals

Fully online, project-based curriculum

Flexible learning hours to fit your schedule, allowing you to balance work, life, and study.

Regular assessments to track your progress and ensure you’re on the right path.

Flex: Self-paced 6 month access

Curriculum

What you’ll learn in this data analytics bootcamp

Download your Coursebook

Intro to Coding Basics (10 hours)

Discover various data roles and their distinctions. Gain insight into data analyst duties versus those of data engineers, scientists, and machine learning engineers. Learn fundamental statistics and tabular data concepts. Understand the significance of data types and collection methods. Familiarize with the course structure, including CT Self Paced model, Slack, Google Classroom, and software installations.

Key Elements of the module include:

  • Understanding Data Roles: Gain insight into various data roles such as data analyst, data engineer, data scientist, and machine learning engineer, including their responsibilities and differences.
  • Statistics and Tabular Data: Learn foundational concepts in statistics and tabular data analysis, including the importance of data in decision-making processes.
  • Data Types and Collection Methods: Understand the differences between continuous and discrete data types, as well as experimental and observational data collection methods.
  • Course Orientation and Tools: Get oriented to the full course structure and curriculum, including an introduction to the CT Self-Paced model of education. Learn how to use communication platforms like Slack and Google Classroom, adhere to course etiquette and norms, and install required software for course participation.

Learning Excel(10 hours)

Learn tabular data with Microsoft Excel! Students will be introduced to the concept of tables and tabular data, how to work in tables, organize data, and more. Students will be able to create spreadsheets and adjust data values with ease, find anomalies, and do basic calculations. We will focus on ease-of-access and shortcuts as well to help make Excel work faster for students.

Key Elements of the module include:

 

  • Introduction to Tabular Data: Understand the fundamentals of tabular data and tables within Microsoft Excel, including how to organize and manipulate data effectively.
  • Spreadsheet Creation and Data Adjustment: Learn how to create spreadsheets and manipulate data values efficiently within Excel, including techniques for organizing data and performing basic calculations.
  • Anomaly Detection: Gain skills in identifying anomalies or irregularities within datasets using Excel’s features and functions.
  • Efficiency Techniques: Explore shortcuts and best practices to increase efficiency and productivity while working with Excel, enabling students to work faster and more effectively.

Basic Statistics(4 hours)

In this module, students will cover beginner and intermediate statistics, including common errors, data types review, and creating a graph selection guide. Topics include distributions, subpopulations, normal distribution, probability, percentiles, correlation, causation, inferential and descriptive statistics, samples, experiments, and evaluating experiments. Also, students will explore hypothesis formation and statistical tools for data analysis.

Key Elements of the module include:

  • Common Statistical Errors: Understand common errors encountered in statistical analysis and how to avoid them, ensuring accuracy in data interpretation and decision-making.
  • Data Types and Visualization: Revisit different data types and learn how to choose the appropriate graph or visualization method for displaying different types of data effectively.
  • Fundamental Concepts: Gain knowledge of key statistical concepts such as distributions, subpopulations, normal distribution, probability, percentiles, correlation and causation, inferential and descriptive statistics, samples, experiments, and evaluation of experiments.
  • Hypothesis Formation and Statistical Tools: Learn how to form hypotheses based on data observations and utilize statistical tools to analyze data and draw meaningful conclusions, empowering students to become proficient data detectives.

SQL (20 hours)

In module 4, learn SQL for building, querying, sorting, and updating relational databases. Students will develop custom functions for automating SQL tasks. Topics include entity relationship diagrams, database planning, and data relationships. Since much tabular data resides in SQL databases, familiarity with SQL is essential. Students will also explore data transfer between SQL and Excel using CSV files. The focus is on establishing a strong foundation in relational databases and gaining intermediate SQL skills through repeated practice. Additionally, advanced SQL techniques and an overview of PostgreSQL and other relational databases will be covered.

Key Elements of the module include:

   

  1. Building and Querying Relational Databases: Learn how to create, query, sort, and update relational databases using SQL, including the development of functions to automate processes.
  2. Entity Relationship Diagrams (ERDs): Understand the importance of designing databases with entity relationship diagrams, emphasizing the significance of proper planning and data relationships.
  3. Data Manipulation and Transfer: Explore techniques for moving data between SQL databases and Excel using CSV files, enabling seamless integration and analysis of tabular data.
  4. Foundational and Intermediate SQL Proficiency: Develop a strong foundational understanding of relational databases and SQL, practicing SQL queries repeatedly to achieve intermediate fluency. Additionally, delve into advanced SQL techniques to enhance data manipulation and analysis capabilities. Gain insights into the workings of PostgreSQL and other relational database management systems.

 

Python(40 hours)

The first part of this module covers basic Python programming, mirroring our full-stack self-paced course. Topics include data types, custom functions, and scripting, providing a comprehensive introduction to Python’s syntax. You’ll explore looping, conditional statements, object-oriented programming, and more, creating Python applications such as shopping carts and interactive games. The latter part focuses on data analysis using NumPy, Pandas, Matplotlib, and introduces Regular Expressions.

Key Elements of the module include:

    

  • Basic Python Programming: Learn fundamental concepts of Python programming, including data types, writing custom functions, looping, and conditional statements, to build a solid foundation in programming.
  • Scripting and Application Development: Dive into scripting with Python, exploring its elegant syntax and applying core programming concepts to develop small applications such as shopping carts and interactive games.
  • Data Analysis with Python Libraries: Explore data analysis capabilities using Python libraries such as NumPy, Pandas, and Matplotlib, enabling manipulation, visualization, and interpretation of data for analytical insights.
  • Regular Expressions: Gain proficiency in using regular expressions within Python for pattern matching and text processing tasks, enhancing your ability to extract and manipulate data efficiently.

R(40 hours)

A programming language specifically meant for use in statistics, we will review most of the concepts in the rest of the course, particularly statistics, in R. Students will learn the basics of the programming language, as well as R’s counterparts to Python’s libraries – Learners will be engaged with ggplot2, tibbles, dplyr, and the tidyverse as well as basic operations, data structures, and more. This module will primarily be used to discuss exploratory data analysis as well.

Key Elements of the module include:

 

  • Introduction to R Programming: Learn the fundamentals of the R programming language, including basic operations, data structures, and syntax, tailored specifically for statistical analysis.
  • R Libraries and Packages: Explore R’s counterpart to Python’s libraries, including ggplot2, tibbles, dplyr, and the tidyverse, for data manipulation, visualization, and analysis tasks.
  • Exploratory Data Analysis (EDA): Dive into exploratory data analysis techniques using R, leveraging its powerful libraries and tools to gain insights into datasets and identify patterns and trends.
  • Application of Statistical Concepts: Apply statistical concepts learned throughout the course, particularly in the realm of statistics, to real-world data analysis tasks using R, reinforcing understanding and proficiency in statistical analysis.

MongoDB, Shell, and more(10 hours)

In this module, students will pick up MongoDB and NoSQL data. We’ll get familiar with writing non-relational data, look at some of the advantages and disadvantages, and get querying using MongoDB’s Compass software. We’ll also briefly look at graph data, which is not part of MongoDB, but is something data heads should be aware of. Students will use Python to pull data from MongoDB and JSON documents hosted on the internet. We’ll also discuss how the cloud works, since MongoDB is a cloud-based database.

Key Elements of the module include:

 

  • MongoDB and NoSQL Fundamentals: Introduction to managing non-relational data with MongoDB.
  • Querying with MongoDB Compass: Hands-on querying and data manipulation using MongoDB’s graphical interface.
  • Python Integration and Cloud Concepts: Utilizing Python for MongoDB interaction and an overview of cloud-based databases.
  • Intro to Coding Basics

    Intro to Coding Basics (10 hours)

    Discover various data roles and their distinctions. Gain insight into data analyst duties versus those of data engineers, scientists, and machine learning engineers. Learn fundamental statistics and tabular data concepts. Understand the significance of data types and collection methods. Familiarize with the course structure, including CT Self Paced model, Slack, Google Classroom, and software installations.

    Key Elements of the module include:

    • Understanding Data Roles: Gain insight into various data roles such as data analyst, data engineer, data scientist, and machine learning engineer, including their responsibilities and differences.
    • Statistics and Tabular Data: Learn foundational concepts in statistics and tabular data analysis, including the importance of data in decision-making processes.
    • Data Types and Collection Methods: Understand the differences between continuous and discrete data types, as well as experimental and observational data collection methods.
    • Course Orientation and Tools: Get oriented to the full course structure and curriculum, including an introduction to the CT Self-Paced model of education. Learn how to use communication platforms like Slack and Google Classroom, adhere to course etiquette and norms, and install required software for course participation.
  • Microsoft Excel

    Learning Excel(10 hours)

    Learn tabular data with Microsoft Excel! Students will be introduced to the concept of tables and tabular data, how to work in tables, organize data, and more. Students will be able to create spreadsheets and adjust data values with ease, find anomalies, and do basic calculations. We will focus on ease-of-access and shortcuts as well to help make Excel work faster for students.

    Key Elements of the module include:

     

    • Introduction to Tabular Data: Understand the fundamentals of tabular data and tables within Microsoft Excel, including how to organize and manipulate data effectively.
    • Spreadsheet Creation and Data Adjustment: Learn how to create spreadsheets and manipulate data values efficiently within Excel, including techniques for organizing data and performing basic calculations.
    • Anomaly Detection: Gain skills in identifying anomalies or irregularities within datasets using Excel’s features and functions.
    • Efficiency Techniques: Explore shortcuts and best practices to increase efficiency and productivity while working with Excel, enabling students to work faster and more effectively.
  • Basic Statistics

    Basic Statistics(4 hours)

    In this module, students will cover beginner and intermediate statistics, including common errors, data types review, and creating a graph selection guide. Topics include distributions, subpopulations, normal distribution, probability, percentiles, correlation, causation, inferential and descriptive statistics, samples, experiments, and evaluating experiments. Also, students will explore hypothesis formation and statistical tools for data analysis.

    Key Elements of the module include:

    • Common Statistical Errors: Understand common errors encountered in statistical analysis and how to avoid them, ensuring accuracy in data interpretation and decision-making.
    • Data Types and Visualization: Revisit different data types and learn how to choose the appropriate graph or visualization method for displaying different types of data effectively.
    • Fundamental Concepts: Gain knowledge of key statistical concepts such as distributions, subpopulations, normal distribution, probability, percentiles, correlation and causation, inferential and descriptive statistics, samples, experiments, and evaluation of experiments.
    • Hypothesis Formation and Statistical Tools: Learn how to form hypotheses based on data observations and utilize statistical tools to analyze data and draw meaningful conclusions, empowering students to become proficient data detectives.
  • SQL

    SQL (20 hours)

    In module 4, learn SQL for building, querying, sorting, and updating relational databases. Students will develop custom functions for automating SQL tasks. Topics include entity relationship diagrams, database planning, and data relationships. Since much tabular data resides in SQL databases, familiarity with SQL is essential. Students will also explore data transfer between SQL and Excel using CSV files. The focus is on establishing a strong foundation in relational databases and gaining intermediate SQL skills through repeated practice. Additionally, advanced SQL techniques and an overview of PostgreSQL and other relational databases will be covered.

    Key Elements of the module include:

       

    1. Building and Querying Relational Databases: Learn how to create, query, sort, and update relational databases using SQL, including the development of functions to automate processes.
    2. Entity Relationship Diagrams (ERDs): Understand the importance of designing databases with entity relationship diagrams, emphasizing the significance of proper planning and data relationships.
    3. Data Manipulation and Transfer: Explore techniques for moving data between SQL databases and Excel using CSV files, enabling seamless integration and analysis of tabular data.
    4. Foundational and Intermediate SQL Proficiency: Develop a strong foundational understanding of relational databases and SQL, practicing SQL queries repeatedly to achieve intermediate fluency. Additionally, delve into advanced SQL techniques to enhance data manipulation and analysis capabilities. Gain insights into the workings of PostgreSQL and other relational database management systems.

     

  • Python

    Python(40 hours)

    The first part of this module covers basic Python programming, mirroring our full-stack self-paced course. Topics include data types, custom functions, and scripting, providing a comprehensive introduction to Python’s syntax. You’ll explore looping, conditional statements, object-oriented programming, and more, creating Python applications such as shopping carts and interactive games. The latter part focuses on data analysis using NumPy, Pandas, Matplotlib, and introduces Regular Expressions.

    Key Elements of the module include:

        

    • Basic Python Programming: Learn fundamental concepts of Python programming, including data types, writing custom functions, looping, and conditional statements, to build a solid foundation in programming.
    • Scripting and Application Development: Dive into scripting with Python, exploring its elegant syntax and applying core programming concepts to develop small applications such as shopping carts and interactive games.
    • Data Analysis with Python Libraries: Explore data analysis capabilities using Python libraries such as NumPy, Pandas, and Matplotlib, enabling manipulation, visualization, and interpretation of data for analytical insights.
    • Regular Expressions: Gain proficiency in using regular expressions within Python for pattern matching and text processing tasks, enhancing your ability to extract and manipulate data efficiently.
  • R

    R(40 hours)

    A programming language specifically meant for use in statistics, we will review most of the concepts in the rest of the course, particularly statistics, in R. Students will learn the basics of the programming language, as well as R’s counterparts to Python’s libraries – Learners will be engaged with ggplot2, tibbles, dplyr, and the tidyverse as well as basic operations, data structures, and more. This module will primarily be used to discuss exploratory data analysis as well.

    Key Elements of the module include:

     

    • Introduction to R Programming: Learn the fundamentals of the R programming language, including basic operations, data structures, and syntax, tailored specifically for statistical analysis.
    • R Libraries and Packages: Explore R’s counterpart to Python’s libraries, including ggplot2, tibbles, dplyr, and the tidyverse, for data manipulation, visualization, and analysis tasks.
    • Exploratory Data Analysis (EDA): Dive into exploratory data analysis techniques using R, leveraging its powerful libraries and tools to gain insights into datasets and identify patterns and trends.
    • Application of Statistical Concepts: Apply statistical concepts learned throughout the course, particularly in the realm of statistics, to real-world data analysis tasks using R, reinforcing understanding and proficiency in statistical analysis.
  • MongoDB, Shell, and more

    MongoDB, Shell, and more(10 hours)

    In this module, students will pick up MongoDB and NoSQL data. We’ll get familiar with writing non-relational data, look at some of the advantages and disadvantages, and get querying using MongoDB’s Compass software. We’ll also briefly look at graph data, which is not part of MongoDB, but is something data heads should be aware of. Students will use Python to pull data from MongoDB and JSON documents hosted on the internet. We’ll also discuss how the cloud works, since MongoDB is a cloud-based database.

    Key Elements of the module include:

     

    • MongoDB and NoSQL Fundamentals: Introduction to managing non-relational data with MongoDB.
    • Querying with MongoDB Compass: Hands-on querying and data manipulation using MongoDB’s graphical interface.
    • Python Integration and Cloud Concepts: Utilizing Python for MongoDB interaction and an overview of cloud-based databases.