Companies are looking for Data Scientists who can manage data, get results, and drive decision-making. Analytics is all about using data to answer questions, and this Career Path will teach you just that. You'll learn how to analyze data, build dashboards, and deliver impactful reports. Along the way, you'll build portfolio-worthy projects that will help you get job-ready. This course includes 69 lessons and 57 projects.
There are 22 Modules in this Data Analytics Career Path:
Topics Covered: Why Data Science?
Topics Covered: Case Studies in Data Literacy, Data Types and Quality, Statistical Thinking, Data Visualization Basics, Misleading and Confusing Graphs, Data Analyses and Conclusions.
Topics Covered: Relational Database Management System, Data Manipulation, Queries, Aggregate Functions, Multiple Tables.
Projects: Create a Table Project, New York Restaurants Project, Trends in Startups Project, Analyze Hacker News Trends Project, Lyft Trip Data Project.
Topics Covered: The Role of Python in Data Science, Python Syntax and Variable Types, Introduction to Functions, Control Flow, Introduction to Lists, Combining Lists, Working with Lists in Python, Working with Python Lists, Python Loops, List Comprehensions, Command Line Interface Setup, Setting up Jupyter Notebook.
Projects: Python Syntax: Medical Insurance Project, Python Functions: Medical Insurance Project, Python Control Flow: Medical Insurance Project, Python Lists: Medical Insurance Costs Project, Python Loops: Medical Insurance Estimate vs. Costs Project
Topics Covered: Introduction to Data Acquisiton, Introduction to Strings, Strings Methods, Python Strings: Medical Insurance Project, Off-Platform Project: Coded Correspondence, Python Dictionaries, Hurricane Analysis Project, Python Dictionaries: Medical Insurance Project, Python Files, Hacking The Fender Project, Off-Platform Project: Reggie's Linear Regression.
Topics Covered: Introduction to Data Manipulation with Pandas, Lambda Functions, Introduction to Pandas and Numpy, Pandas, Creating, Loading, and Selecting Data with Pandas, Modifying DataFrames, Petal Power Inventory Project, Aggregates in Pandas, A/B Testing for ShoeFly.com Project, Working with Multiple DataFrames, Dataframe Merge with Pandas, Page Visits Funnel Project, This is Jeopardy! Project.
Topics Covered: Introduction to EDA, Variable Types, Census Variables Project, Inspect, Clean, and Validate a Dataset, EDA:Diagnosing Diabetes Project, Summary Statistics, Exploring Student Data Project, Associations between Quantitative and Caegorical Variables, Associations between Two Quantitative Variables, Associations between Two Categorical Variables, NBA Trends Project.
Topics Covered: Introduction to Probability, Proability, Set Theory, and the Law of Large Numbers, Rules of Probability, Probability Distributions, Detecting Product Defects with Probability Project, Sampling Distributions, The Central Limit Theorem, Sampling Distributions Dance Party! Project, Inferential Statistics, Introduction to Hypothesis Testing, One-Sample T-Tests in SciPy, Simulating a Binomial Test, Significance Thresholds, Heart Disease Research Part 1 Project, Introduction to Linear Regression, Linear Regression with a Categorical Predictor, Linear Regression at Codecademy Project.
Topics Covered: Line Graphs in Matplotlib, Sublime Lime's Line Graphs Project, Different Plot Types, Recreate Graphs using Matplotlib, The Data visualization Catalogue, Bar Charts and Pie Charts, Exploring Mushrooms Project, Visualizing Multivariate Relationships, Visualizing Time Series Data with Python, Data Visualizations for Messy Data, Airline Analysis Project, Data Visualization Best Practices.
Topics Covered: Introduction to Data Wrangling and Tidying, Introduction to Regular Expressions(RegEx), How to Clean Data with Python, Cleaning US Census Data Project, Introduction to Handling Missing Data, Types of Missing Data, Handling Missing Data with Deletion, Single Imputation, Multiple Imputation, Off-Platform Project: Stack Overflow Survey Trends.
Topics Covered: Structure of a Data Analysis Report, Audience Analysis, How to Write Data Analysis Reports.
Topics Covered: Data Scientist Career Path Specializations, How to Showcase Your Data Science Skills.
Topics Covered: Introduction to Advanced EDA, Variance, Standart Deviation, Variance in Weather Project, Histograms, Traveling to Acadia Project, Describe a Histogram, Describe Exam Distributions Project, Quartiles, Quantiles, Interquantile Range, Life Expectancy By Country Project, Boxplots, Healthcare in Different States Project, Summary Statistics for Categorical Variables, Summarizing Automobile Evaluation Data Project, Data Centering and Scaling, Discretizing Numerical Data and Collapsing Categories, Advanced Data Transformations, Startup Transformation Project.
Topics Covered: Introduction to Visualization for Data Science Applications, Constellations Project, Borad Slides for FoodWheel Project, Roller Coaster Project.
Topics Covered: Introduction to Advanced SQL for Data Science, Window Functions, Math and Date Functions, Climate Change Project, Analyze Real Data with SQL, Usage Funnels, Usage Funnels with Warby Parker Project, Calculating Churn, Calculating Churn Rates Project, First and Last Touch Attribution, Attribution Queries Project, Analyze Twitch Caming Data Project, Visaulize Twitch Gaming Data Project.
Topics Covered: Data Setup, NYC Census and Income: Data Setup Project, Making Visualizations in Tableau, NYC Census and Income: Data Visualization, More on Storytelling, Airplane Wildlife Strikes Project.
Topics Covered: Download and Install Excel, Exploring Data, Pivot Tables, Explore GDP in Excel Project, Visualizing Data, Visualizing Hotel Data in Excel, Handling Data, Handling Hotel Data in Excel, Analyze Bitcoin Data in Excel Project
Topics Covered: Next Steps after the Data Scientist: Analytics Specialist Career Path
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