
Data Scientist, Data Analyst, Business Analyst, and Data Engineer. These are the typical roles in the data science world. Do you know which position you want? Will this activity change your intentions for the future? Did you guess right?
What is Data Science?
A data scientist can produce a few different types of 'products' depending on the need.
I go over the 5 V's, which explain what we mean by Big Data.
Data Mining is an attempt to find patterns in data by using different techniques.
I distinguish between machine learning and deep learning.
A few points of advice for those wanting to start a career in Data Science!
Four programming languages that are vital to learn if you are interested in Data Science.
I suggest that everyone learns Python because it is the most popular language, it has clean and readable syntax, and is great if you are not sure what you want to focus on in Data Science quite yet.
I suggest learning SAS if you are in USA, India and Brazil, if you are interested in Business Analytics/Advanced Analytics, and you intend on working for Fortune 200 companies.
If you are in Academia, this is the language you should focus on.
SQL is largely used to fetch data from relational databases.
I introduce you to the Data Science process and the different steps that take place.
Frame a case study for a U.S. health insurer to allocate a limited budget and maximize quality care by clarifying goals and readmission risks after acute myocardial infarction.
Split the data into a training set to build models predicting readmission. Compare classification algorithms like logistic regression and decision trees, emphasizing interpretability for actionable insights.
Learn how to evaluate classification models using the confusion matrix and metrics like accuracy, precision, and recall; understand false negatives, bias-variance tradeoffs, and the train/test split.
Discover machine learning in action with the IBM Watson assistant, a machine learning as a service chatbot, and learn how data scientists collaborate with developers to automate basic data tasks.
Learn to build a health care chatbot with a Watson assistant by creating an assistant, adding a dialogue skill, and configuring welcome and anything else nodes to handle user input.
Create an appointment type entity with values like medical care, dental, and psychological, add synonyms and fuzzy matching, and connect it to dialogue to identify requests and train the model.
Explore Python libraries for numerical computing, data cleaning, and visualization—NumPy, Pandas, Matplotlib, Seabourne. Learn machine learning with Saikat learn and Karash, plus tensor flow; explore R libraries and SAS base.
Explore github as a code hosting platform for version control and collaboration, track changes, review code, add comments, suggest changes, assign tasks, and back up your work.
Discover how to create branches and commit changes, using the main production branch and feature branches within the GitHub flow.
Learn to create and review pull requests on GitHub, compare branches, view diffs, and merge changes from a readme edits branch into the main branch, then delete the branch.
Welcome! If you see Data Science as a potential career in your future, this is the perfect course to get started with.
Our course does not require any previous data science experience. The goal of 'Data Science for Beginners' is to get you acquainted with data science methodology, data science concepts, and programming languages; give you a peek into how machine learning works; and finally show you a data science tool like GitHub, which lets you collaborate with your colleagues.
Now, while this is a beginner course, it does not mean it is easy. For example, in the Data Science methodology section, many different concepts are introduced. But please keep in mind that a. you will get concrete examples of what each concept means when it is brought up b. you can ask questions in the Q and A and c. most importantly, you are not meant to understand all the concepts. Going through the methodology is meant to introduce you to concepts, not prepare you to apply them fully. You will have a chance to do this in other courses (ours or those offered by other providers).
Beyond this, you will get to build a simple chatbot. This hands-on activity will illustrate, in a more interactive way, how machine learning works and how you can provide a machine learning service like this in your future career.
So, don't hesitate. Start your data science learning journey today!