An overview of the (former version) Datacamp Data Scientist Certification in Python

• Personal certification page: https://www.datacamp.com/certificate/DS0017278428696

• 23 courses, 6 projects, 3 skill assessments

1. Introduction to Python
   Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with Numpy.
2. Intermediate Python
   Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with Pandas
   
   PROJECT: Investigating Netflix Movies and Guest Stars in the Office
   
   
3. Data Manipulation with pandas
   Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
   
   PROJECT: The Android App Market on Google Play
   
   
4. Joining Data with Pandas
   Learn to combine data from multiple tables by joining data together using pandas
   
   PROJECT: The Github History of the Scala Language
   
   
5. Introduction to Data Visualization with Matplotlib
   Learn how to create, customize, and share data visualizations using Matplotlib.
6. Introduction to Data Visualization with Seaborn
   Learn how to create informative and attractive visualizations in Python using the Seaborn library.
7. Python Data Science Toolbox (Part 1)
   Learn the art of writing your own function in Python, as well as key concepts like scoping and error handling.
8. Python Data Science Toobox (part 2)
   Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
9. Intermediate Data Visualization with Seaborn
   Use Seaborn’s sophisticated visualization tools to make beautiful, informative visualizations with ease.
   
   PROJECT: A Visual History of Nobel Prize Winners
   
   SKILL ASSESSMENT: Data Manipulations with Python
   
   
10. Introduction to Importing Data in Python
    Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
11. Intermediate Importing Data in Python
    Improve your Python data importing skills and learn to work with web and API date
12. Cleaning Data in Python
    Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
13. Working with Dates and Times in Python
    Learn how to work with dates and times in Python
    
    SKILL ASSESSMENT: Importing & Cleaning Data with Python
    
    
14. Writing Functions with Python
    Learn to use best practices to write maintainable, reusable, complex function with good documentation.
    
    SKILL ASSESSMENT: Python Programming
    
    
15. Exploratory Data Analysis in Python
    Learn how to explore, visualize, and extract insights from data
16. Analyzing Police Activity with pandas
    Explore the Standford Open Policing Project dataset and analyze the impact of gender on police behaviour using Pandas
17. Statistical Thinking in Python (Part 1)
    Build the foundation you need to think statistically and to speak the language of your data.
18. Statistical Thinking in Python (Part 2)
    Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
    
    PROJECT: Dr. Semmelwels and the Discovery of Handwashing
    
    
19. Machine Learning with scikit-learn
    Learn how to build and tune predictive models and evaluate how well they’ll perform on unseen data
    
    PROJECT: Predicting Credit Card Approvals
    
    
20. Unsupervised Learning in Python
    Learn how to cluster, transform, visualize, and extract insights from unlabelled datasets using scikit-learn and scipy.
21. Machine Learning Tree-Based Models in Python
    In this course, you’ll learn how to use tree-based models and ensembles for regression and classification using scikit…
22. Case Study: School Budgeting with Machine Learning in Python
    Learn how to build a model automatically classify items in a school budget.
23. Cluster Analysis in Python
    In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means c…

• Four 40-minute timed assessments

1. Coding for Production
2. Statistical Experimentation
3. Exploratory Analysis with PostgreSQL
4. Model Development

• One coding challenge  

• Final case study¹

• Two parts:
  • Technical report for data science manager
  • Presentation for non-technical audience
• Problem type: Binary classification
• Work environment: Datacamp workspace
• Libraries/modules used: pandas, numpy, seaborn, matplotlib, BeautifulSoup, nltk, PorterStemmer,
  TfidfVectorizer, WordCloud, operator, plotly, sklearn, time, imblearn, LogisticRegressionCV, RandomForestClassifier, classification_report, confusion_matrix, balanced_accuracy_score, matthews_corrcoef, geometric_mean_score, compute_class_weight, GridSearchCV
• Workflow:
  • Read CSV data: >40000 entries
  • Data exploration
  • Subset data for NLP analysis
  • Data quality: duplicates/missing data/quality check e.g. mojibake
  • Feature engineering e.g. combining non-numerical columns
  • Text preprocessing (e.g. stemming, tokenization, TF-IDF)
  • Data summary and visualizations e.g. word clouds, distribution
  • Stratified train-test split
  • Machine learning algorithms with class weights: compare metrics, select best, hyperparameter tuning (gridsearchcv + adjust decision threshold) to answer business question as well as achieve business success criteria.
  • Summary/result/discussion: Findings, final model, metrics trade-off
  • Recommendations for future work: SME involvement, better data quality e.g. well defined features, better data representativeness e.g. additional features, other methods e.g. deep learning

Footnotes

1. Restrictions on sharing as advised by DataCamp ↩