Data Analytics

Topic 1: Prep class

Before moving on to the core sections of the curriculum, you’ll work through exercises that will help you familiarize yourself with Python, the most popular programming language for data science tasks, and get a crash course in statistics from Khan Academy.

Topic 2: What is Data Science?

In this opening unit, you’ll receive an overview of the Data Science Method and learn the skills needed to thrive in the field. You’ll hear about your day-to-day work duties including data cleaning and building models from those in the field.

• Learn about key data science skills
• Understand the six steps to the Data Science Method

Topic 3: Problem Identification

To start a data science project, you need to know the problem you need to solve, clearly define it, then break the problem down into manageable pieces.

• You’ll work through the first step of the data science method — identify the correct problem to solve and set goals for a project.
• Work through SMART problem statements
• Fill out problem statement worksheets

Topic 4: Python for data science

Python is a must-have programming skill in the data science world. You’ve already laid the foundation in pre-work, but this unit will teach you the language in-depth and also help you leverage pandas for data cleaning and manipulation.

• Follow coding best practices in Python
• Learn Python data types, foundations, and standard libraries
• Learn Pandas

Topic 5: Data science method application

This unit gives you an introduction to the steps of the Data Science Method (DSM) and closes with a guided capstone project where you’ll present to stakeholders.

• Familiarize yourself with the six steps of the Data Science Method
• Learn problem identification, data wrangling, exploratory data analysis, pre-processing and training data development, modeling, and documentation
• Complete a guided capstone encapsulating steps in the DSM and presenting findings to executives

Topic 6: Data Wrangling

This unit explores wrangling — or how to clean, organize, and structure raw data — in a hands-on way by having you wrangle data. Identify a suitable dataset for sales analysis, which could include information such as product details, sales quantity, prices, customer demographics, etc.

• Submit ideas and a project proposal
• Review data types, build data profiles, and develop and understand your data’s features

Topic 7: SQL and Databases

You’ll learn the inner workings of Structured Query Language (SQL) to query relational database management systems. Querying helps you understand the data contained in the databases. You’ll work through DataCamp courses and then a case study.

• Learn the landscape of SQL and databases
• Write queries in SQL
• Work with relational databases in Python

Topic 8: Statistics for Exploratory Data Analysis

Statistics is the mathematical foundation of data science. It allows you to draw useful conclusions from data. In this unit, you’ll learn concepts from David Spiegelhalter’s book, “The Art of Statistics.” You’ll read through one or two chapters, work on an exercise, test your knowledge with a quiz, and review takeaway notes.

• Become equipped with essential conceptual knowledge before diving into application statistics
• Assess uncertainty through resampling
• Learn probability theory and hypothesis testing
• Delve into advanced statistics

Topic 9: Python Statistics in EDA

Inferential statistics is a set of techniques that helps you identify significant trends and characteristics of a data set. Not only is it useful to explore the data and tell a good story, but it also paves the way for deeper analysis and actual predictive modeling. In this unit, you’ll learn several inferential statistics techniques, then take your learnings and apply the Exploratory Data Analysis (EDA) step to your second capstone.

• Transfer statistical concepts into practical skills and learn how to implement statistical concepts in Python
• Take a deep dive into statistical inference, hypothesis testing, and statistical modeling in Python
• Incorporate learning from data visualization in Python

Topic 10: Machine Learning Overview

Machine learning combines aspects of computer science and statistics to extract useful insights and predictions from data. In this unit, you’ll begin to learn the foundations of machine learning and understand best practices and common challenges when working on machine learning applications.

• Explore the fundamentals of machine learning
• Gain an understanding of the taxonomy of different types of ML algorithms
• Develop an understanding of best practices and common challenges that data scientists deal with when working on machine learning applications

Topic 11: Supervised Learning

Supervised learning is one of the most commonly used forms of machine learning. In supervised learning, you give the machine your labeled training data and encode procedures for the machine to learn to assign those labels itself.

• Develop an understanding of supervised learning and its common application
• Be able to perform regression and classification techniques to solve real-world problems

Topic 12: Unsupervised Learning

Unsupervised learning requires minimal human supervision. Unlike supervised learning, the machine looks for patterns in a dataset with no pre-existing labels. In this unit, you’ll perform clustering techniques and then complete a case study on k-clustering.

• Develop knowledge of common clustering types
• Be able to perform clustering techniques to solve real-world problems
• Complete a distance metrics exercise and a cosine similarity exercise

Topic 13: Feature Engineering

Feature engineering consists of converting data into a feature matrix to look for patterns and create features from raw data. It’s a vital skill that improves the performance of machine learning models. In this unit, you’ll work through completing exercises and honing the pre-processing and training data development side of the DSM.

• Perform data transformation for categorical features, image features, and text features
• Learn best practices for deriving features, handling missing data, and automated feature engineering
• Apply feature engineering techniques to step four of your second capstone: pre-processing and training data development

Topic 14: Machine Learning Applications

Furthering your understanding of machine learning, this unit takes you behind the scenes of modeling metrics and hyperparameter tuning. You’ll complete exercises on model evaluation metrics and learn which model metric to use based on the business problem you’re trying to solve. You’ll also learn how hyperparameter tuning can make or break your model. You’ll finish up the unit by working on the modeling stage in capstone two.

• Take a deep dive into the types of evaluation metrics for regression and classification
• Be able to choose the best evaluation metric for your machine learning project
• Learn best practices for model optimization

Topic 15: Data Storytelling

A data story is a powerful way to present insights to your clients, combining visualizations and text into a narrative. This final core unit will get your creative juices flowing by suggesting some interesting questions you can ask of your dataset. You’ll also execute the last stage of the DSM (Documentation) by developing a final project report.

• Learn how to apply presentation techniques for executive (C-suite), technical, and non-technical audiences
• Prepare a presentation about a dataset of your choosing
• Finalize the documentation of your second capstone project
• Give a presentation about the work you completed for your second capstone

Topic 16: Specialization Tracks

Hone your skills in a specific area of expertise by choosing one of our three specialization track options. You’ll be able to talk to your mentor and career coach before deciding.

Option 1 — Generalist Track:
If you’re interested in gaining a wide range of skills that will help you land a job in various industries (and in various locations), the Generalist Track may be right for you.

Option 2 — Business Insider Track:
If you’re keen to learn how to draw business-focused insights from data and make actionable recommendations that can impact the company you work for, the Business Insider Track may be right for you.

Option 3 — Advanced Machine Learning Track:
If you loved the machine learning units and want to continue to learn advanced machine learning skills, including how to deploy a model to production, then the Advanced Machine Learning Track may be the right choice.

Topic 17: Soft Skills Training

A lot of companies are not just looking for the technically skilled but for good all round professionals. At LM Tech Hub giving our students the right soft skills for the job market is in our DNA. Our students learn how to communicate effectively, work as a team player, learn how to learn and how to adapt to change.

Topic 18: Projects

You’ll work on three capstone projects to give you the hands-on knowledge of working like a data scientist.

Capstone 1: You’ll be introduced to the six steps of the Data Science Method (DSM) early on in the program, then execute each of these important steps through guidance from your instructor. You’ll practice each step before applying your knowledge to your second capstone.

Capstone 2: Similar to the guided capstone one, you’ll execute the steps of the DSM but with less guidance. You’ll develop a project idea and proposal, find and wrangle data, use exploratory data analysis techniques, pre-process and create a training dataset, build a working model, then document and present your work. You’ll submit each step separately.

Capstone 3: Capstone 3 runs through the steps of the DSM, but you’ll choose your project idea depending on the specialization track you’re enrolled in.