Understanding the World Through Data
This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features lecture videos, lecture exercises, and problem sets using Python 3.5. Even if you previously took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning.
At a glance
- Institution: MITx
- Subject: Computer Science
- Level: Introductory
- Prerequisites:
High school algebra and a reasonable aptitude for mathematics. Students without prior programming background will find there is a steep learning curve and may have to put in more than the estimated time effort.
- Language: English
- Video Transcript: English
- Associated programs:
- XSeries in Computational Thinking using Python
- Associated skills: Sales, Data Science, Computer Science, Computational Thinking, Python (Programming Language)
Syllabus
Module 1: How to represent and manipulate data
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- Examples of numerical data
- The Python programming language and the Colab notebook programming environment
- Loading datafiles in Colab as dataframes and performing simple operations (selecting rows or columns, filtering data by specific conditions, grouping data, applying functions on the resulting groups)
- Finding the correlation between columns of the dataframe
- Visualizing the data using line plots, scatter plots, histograms, correlation matrix
Module 2: Reverse engineering nature
- Dependent and independent variables and how they correspond to real life scenarios
- Intuition for what a linear model is
- Intuition for what a polynomial model is
- Python libraries that can perform the linear regression on data
- Compare the quality of different models (mean-squared-error and R^2 values)
- Fitting higher order polynomials
- Overfitting
Module 3: Distributions and Latent Variables
- Uniform distributions
- Gaussian distributions
- Distribution mean and standard deviation
- Noise in distributions (biased and unbiased noise)
Module 4: How machines think
- Categorizing data based on particular conditions being met
- Using linear regression to classify a new datapoint as above or below the best fit line
- Using a support vector classifier to separate two groups of data and classifying a new datapoint into a group
- Using logistic regression to classify data into two groups and finding the probabilities of a new datapoint falling into each group
- Understanding how to divide data into training and test sets
How to Enroll:
- Choose your desired certificate program on the IBM website.
- Create an Amazon if you don’t have one.
- Select specific courses within your chosen program.
- Enroll in courses, and pay if necessary.
- Access course materials and complete requirements.
- Prepare for and take certification exams if required.
- Earn your certificate upon successful completion.
- Be aware of maintenance or renewal requirements, if applicable.
Enroll Now
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