This repo contains most of the coding labs completed as part of the Data Analytics Bootcamp at Ironhack (full-time cohort, April-June 2022).
The repo is organized following the 3 modules of the bootcamp (see full index below)
| Module | Content |
|---|---|
| Module 1 | Introduction to Data analytics and Python (data wrangling & cleaning, API & Web scraping, Git, SQL, Python) |
| Module 2 | Advanced Data Analytics (statistics & probability, inferential statistics, hypothesis testing, Tableau) |
| Module 3 | Machine Learning Fundamentals (supervised & unsupervised learning; build, train, and evaluate ML models) |
| Week | Labs | Language | Description |
|---|---|---|---|
| 1 | intro-to-pandas | Python | Pandas (Series and DataFrames), how to work with them, how to obtain them from other data structures, and how to perform basic calculations with them. |
| 1 | list-comprehension | Python | Constructing list comprehensions and using them to extract and filter information in a variety of scenarios. |
| 1 | numpy | Python | Introduction to NumPy array. |
| 1 | string-operations | Python | Practice how to manipulate strings, and to use string manipulation techniques to create Bag of Words (BoW). |
| 1 | tuple-set-dict | Python | Practice Python native data structures and become proficient at using them. |
| 2 | advanced-pandas | Python | Practice advanced functions, changing the index and method chaining in Pandas. |
| 2 | data-cleaning | Python | Practice data cleaning techniques. |
| 2 | dataframe-calculations | Python | Refining problem-solving process. Breaking down a complex problem into a subset of less complex problems, then tackle each sub problems in a progressive order. |
| 2 | import-export | Python | This lab discuss the task of importing and exporting data into pandas using different file formats. |
| 2 | sql-first-queries | SQL | Practice SQL queries to answer some questions. |
| 2 | mysql-select | SQL | Practice how to use the MySQL SELECT statement. |
| 2 | mysql | SQL | Practice how to design, create, and manage a database. |
| 3 | advanced-regex | Python | Practice how to put together regular expression. |
| 3 | api-scavengers | Python | Practice how to make requests to APIs and parse the JSON responses to extract the information we need. |
| 3 | matplotlib-seaborn | Python | Create different types of visualizations using matplotlib and seaborn: bar charts, scatter charts and box plots among many others. |
| 3 | pandas-deep-dive | Python | Perform a variety of operations using the Pandas library. |
| Week | Labs | Language | Description |
|---|---|---|---|
| 4 | descriptive-stats | Python, Statistics | Practice better understanding of basic descriptive statistics, how to compute the basic descriptive metrics and compare them in different use cases. |
| 4 | pivot-table-and-correlation | Python | Practice Pandas pivot table to extract insights from data, and to describe the strength and direction of the relationship between two variables. |
| 4 | regression-analysis | Python, Statistics | Apply different types of regressions, and use them to understand the trends in data, and predict future values of the outcome. |
| 4 | subsetting-and-descriptive-stats | Python, Statistics | Use Pandas library to extract insights from your data by dividing it into into several subsets, and use Pandas descriptive statistics functions. |
| 5 | mini-project | Python, Statistics | Review the concepts of Inferential Statistics. Clean data, do Exploratory Data Analysis, and do hypothesis testing. |
| 5 | confidence-intervals | Python, Statistics | Apply the knowledge on confidence intervals, using normal, student's t and chi-squared distributions. |
| 5 | hypothesis-testing-1 | Python, Statistics | Construct one sample hypothesis test and confidence intervals. |
| 5 | hypothesis-testing-2 | Python, Statistics | Construct one sample hypothesis test and confidence intervals. |
| 5 | intro-probability | Python, Statistics | Tackle some basic probability questions. |
| 5 | probability-distribution | Python, Statistics | Practice probability distribution to discover meaningful relationship between events and make better data-driven decision. |
| Week | Labs | Language | Description |
|---|---|---|---|
| 7 | intro-to-ml | Python | Practice how to properly prepare the data for ML algorithms. |
| 7 | supervised-learning-feature-extraction | Python | Explore the techniques to extract meaningful information from data, by transforming the data using derived columns, grouping the data and using aggregated information, or cleaning and reformatting the data. |
| 7 | supervised-learning-sklearn | Python | Explore the scikit-learn library in the context of supervised learning. |
| 7 | supervised-learning | Python | Predict malicious vs benign websites using supervised learning. |
The content and org of this repo have been inspired by our amazing TA at Ironhack Lisbon Gladys Mawarni and other former bootcamp students.