Data Analyst

Introduction (1 week) Introduction project: Prepare the workbench In this project, you will learn how to setup your working environment. Your objective is to become familiar with modern data analytics tools, version control repositories and Amazon Web Services. Week 1 - Data Analyst toolbox ● Learn how to use GitHub, creating your first repository ● Setup AWS account, introduction to S3 and RDS services ● Familiarize with Markdown and JSON technologies
Unit I - Data Extraction and Manipulation (6 weeks) Introduction project: Explore payments transactions In this project, you will analyze payment transactions from an online company. You will compute summary statistics and filter out refunded payments. Your objective is to build weekly revenue report which progressively disclose details level. Week 2 - Spreadsheets as a data tool ● Retrieve dataset into Spreadsheet from Amazon S3 storage through API ● Usage of pivot tables and pivot charts for exploratory analysis ● Combine and unify data sources Project: Investigate the dataset In this project, you will connect to operational database of one our partners. You will learn how to navigate over the database. Your objective is to write queries to retrieve data and enrich the reports additional information.
Week 3 - SQL basic concepts ● Connect your SQL Client to the remote database ● Understand table structure - projection and selection ● Summarize data aggregations Week 4 - Advanced SQL Queries ● Combine data from different relations - join ● Create dynamic data subsets ● Learn how to clean up your code - Views
Project: Subscription churn In this project you will be provided with the dataset reflecting account status for online subscription company (SaaS). You will analyze multiple scenarios of customers which decide to renew or cancel the subscription. Your objective will be to figure out and propose actions to increase retention. Week 5 - Introduction to R ● Get familiar with R environment ● Retrieve data from files, API, SQL databases ● Use data frames Week 6 - Data wrangling and visualization with R ● Summarize, aggregate and combine data with ddply ● Build advanced visualizations with ggplot ● Structure your code with functions
Project: Wrangle data with Python Pandas In this project you will use Python environment to create automated monthly recurring revenue (MRR) report. You will write code that retrieve payment transactions from a database and distribute them across the corresponding months. You will end-up with a Python notebook that presents on-demand MRR results. Week 7 - Python, Pandas ● Get familiar with Python Notebook environment ● Load data for the analysis ● Wrangle your data with Python Pandas dataframes
Unit II - From Data to Metrics (3 weeks) Project: Data Language vs Business Speech In this project, you will work in a data visualization environment building real time reports connected to a database. Your objective is choose and implement the most appropriate visualization for every underlying metric. Week 8 - Online company KPI ● Structure - KPIs, first, second, operational levels ● Lead and Lag indicators ● Trend evolution and benchmarking Week 9 - Metric analytics ● Importance of the Cohort Analysis ● Funnels and Conversion Goals ● Customer value in time
Project: Face the CEO In this project you will design a dashboard that represents first level metrics for an e-commerce company. You will share your dashboard to one of our partners CEO and gather feedback on your presentation. Week 10 - Actionable Insights ● User Analytics and company goals ● Decision making with data insights ● Storytelling and executive summaries
Unit III - A/B Testing (2 weeks) Project: Improve the conversion rate In this project you will work with conversion rate of the e-commerce company. You will be provided with data for several versions of the website design. Your objective is to examine experiment results and translate them into actionable proposals. Week 11 - Practical statistics ● Distributions: means and proportions ● Hypothesis testing ● Estimators, confidence interval, p-value Week 12 - Real world A/B testing ● Test design and execution ● Setting primary and secondary objectives ● Choosing sample size
Unit IV - Introduction to Business Intelligence (2 weeks) Project: Work with millions of data points In this project, you will access data warehouse with more than 100 millions data points that represent user behaviour of the SaaS product. Your objective will be to find common usage pattern and correlate them with subscription churn. Week 13 - Dimensional Data Models ● Understand dimensional model: metrics and dimensions ● Analyze data in a star and snowflake schemas ● Learn about normalized and denormalized data forms Week 14 - Large scale Data Warehouses ● Face terabyte - scale data models ● Work with time-series data stores ● Analyze events data - append only model
Unit V - Online Data Analytics (2 weeks) Project: Know your customers In this project, you will analyze data gathered from the visits in e-commerce website. You will work in Google Analytics environment identifying the most and least efficient audiences. You will include spent data from a digital advertisements constructing segmentation model for customer acquisition cost. Week 15 - Google Analytics basics ● Google Analytics functionality ● Navigation, views filters configuration ● Basic reports: audiences, acquisition and behaviour Week 16 - Digital Advertising data ● AdWords campaigns analysis ● Tag Managers and URL tracking ● Business goals, conversion measuring
Assessment (4 weeks) The course finishes with the assessment project where you put all your projects together building 360 company view. The assessment project is meant to become a showcase in your data analyst portfolio. You will use the project to prepare your pitch and share it with our partner companies.