IBM Data Science Professional Certification

Access web NBA data using APIs and web scraping from Python in Jupyter Notebooks.

Using Jupyter notebook with SQL to collect and analysed census, crime, and school data for a given neighbourhood. Identifying causes with a multiple linear regression model that impacts the enrolment, safety, health, environment ratings of schools. Striving to improve educational outcomes for children and youth in the City of Chicago.

Use JupyterLabs, Python (libraries: pandas, numpy, seaborn and matplotlib) to predict the market price of a house given a set of features of the houses e.g. zip codes, bedrooms, bathrooms etc. Achieved using a linear regression model. Then preformed a second order polynomial transformation on the training data and testing data. To identify the curvilinear relationship between independent and dependent variables as the data was non-linear.

Learn the rudiments of machine learning techniques such as Regression (Linear Regression, Multiple Linear Regression), Classification techniques (K-Nearest Neighbours, Decision trees and Regression trees), Linear Classification (Logistic Regression) and Clustering with K-Means.

Utilise Python (Pandas, Beautiful Soup) to extract Tesla and GameStop financial data to build an automated/live dashboard comparing stock price and company financial metrics.

Use SQL and SpaceX API to select and sort SpaceX Falcon 9 launch data. Conducted EDA in JupyterLabs (python), constructed dashboard to analyse launch records interactively with Plotly and launch site proximity with Folium. Split data into test and training sets. Tuned hyperparameters for several models (Logistic Regression, Support Vector Machines, Decision Tree Classifier and L=K-Nearest Neighbours) and deployed models to predict successful landing of Falcon 9 rocket. Communicate findings in a powerpoint.

• Define data science and its importance in today’s data-driven world.
• Describe the various paths that can lead to a career in data science.
• Summarize advice given by seasoned data science professionals to data scientists who are just starting out.
• Explain why data science is considered the most in-demand job in the 21st century.

• Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
• Utilize languages commonly used by data scientists like Python, R, and SQL
• Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features
• Create and manage source code for data science using Git repositories and GitHub.

• Describe what a data science methodology is and why data scientists need a methodology.
• Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
• Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
• Determine appropriate data sources for your data science analysis methodology.

• Learn Python - the most popular programming language and for Data Science and Software Development.
• Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.
• Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.
• Access and web scrape data using APIs and Python libraries like Beautiful Soup.

• Play the role of a Data Scientist / Data Analyst working on a real project.
• Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.
• Apply Python fundamentals, Python data structures, and working with data in Python.
• Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

• Analyze data within a database using SQL and Python.
• Create a relational database and work with multiple tables using DDL commands.
• Construct basic to intermediate level SQL queries using DML commands.
• Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

• Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data
• Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy
• Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
• Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

• Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story
• Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble
• Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps
• Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library

• Describe the various types of Machine Learning algorithms and when to use them
• Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression
• Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 
• Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

• Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders
• Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation
• Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors
• Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model

"The IBM Data Science Professional Certification is an accessible and comprehensive online course ideal for beginners, requiring no prior experience and offering flexible learning at 10 hours a week. It includes a broad range of topics from Python programming to machine learning techniques, with practical assignments in Jupyter Notebooks. The course offers good value at £29 per month on Coursera, and successfully completing it can bolster one's CV, as evidenced by the author's transition to a Masters in Data Science and a career in Data Analysis. However, it lacks depth in machine learning theory and data engineering skills, which may require further learning." - Mr GPT