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### The Golden Phase of Learning Data Science and becoming a Data Scientist through Online Lectures

- Secondary school (high school) algebra
- Ability to work with tables, formulas, and charts in Excel
- Ability to organize and summarize data using Excel analytic tools such as tables, pivot tables, and pivot charts
- Excel 2016 is required for the full course experience. Excel 2013 will work but will not support all the visualizations and functions

- Descriptive statistics
- Basic probability
- Random variables
- Sampling and confidence intervals
- Hypothesis testing

- Fundamental R programming skills
- Statistical concepts such as probability, inference, and modeling and how to apply them in practice
- Gain experience with the tiny verse, including data visualization with ggplot2 and data wrangling with dplyr
- Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
- Implement machine learning algorithms
- In-depth knowledge of fundamental data science concepts through motivating real-world case studies

- Understand Python language basics and apply to data science
- Practice iterative data science using Jupyter notebooks on IBM Cloud
- Analyze data using Python libraries like pandas and Numpy
- Create stunning data visualizations with mat-plot-lib, folium and seaborn
- Build machine learning models using Scipy and sci-kit learn
- Demonstrate proficiency in solving real-life data science problems

- Basic R syntax
- Foundational R programming concepts such as data types, vectors arithmetic, and indexing
- How to perform operations in R including sorting, data wrangling using dplyr, and making plots

- Important concepts in probability theory including random variables and independence
- How to perform a Monte Carlo simulation
- The meaning of expected values and standard errors and how to compute them in R
- The importance of the Central Limit Theorem

- How to analyze data and perform simple data visualizations using ProcessingJS
- Understand and apply introductory programming concepts such as sequencing, iteration, and selection
- Equip you to study computer science or other programming languages

- Write Python programs to solve various tasks you may encounter
- Formulate a formal computational problem from an informal biological problem
- Develop algorithms for solving computational problems
- Evaluate the effectiveness of algorithms
- Apply existing software to actual biological datasets

- The previous course in the MicroMasters program: DSE200x
- Undergraduate level education in:
- Multivariate calculus
- Linear algebra

- The history of data science, tangible illustrations of how data science and analytics are used in decision making across multiple sectors today, and expert opinion on what the future might hold.
- A practical understanding of the fundamental methods used by data scientists including; statistical thinking and conditional probability, machine learning and algorithms, and effective approaches for data visualization.
- The major components of the Internet of Things (IoT) and the potential of IoT to totally transform the way in which we live and work in the not-to-distant future.
- How data scientists are using natural language processing (NLP), audio and video processing to extract useful information from books, scientific articles, twitter feeds, voice recordings, YouTube videos and much more.