5 Steps to Get Started With Data Science

As a beginner it is easier to get lost in the details and shear overwhelming nature of learning machine learning.

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Cross the beginner’s block

I get a lot of mails from readers asking.

  • How do I get started with Machine Learning? 
  • I do not have a background in math, how can I learn data science?                        

More often the materials on blog posts and courses are often targeted at intermediates. But remember it is easier to get started without the math. You would still need the math, but it can come later. Below is a step by step guide to get started, but remember..

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Be Curious

When I first started with machine learning, I started reading anything that had the title data science/machine learning. I often did not understand most of it, but slowly I started to grow chunks of knowledge which I later assembled. The important skill here is to be curious and believe.

Learn a tool

Never get overwhelmed with a choice of tool. Just pick one!. Often beginners are divided between R and Python. Here are a list of resources to get started with the tools.

Python

R

Get your hands dirty

The best place for a good data source would be the  UCI Machine Learning Repository.  The repository is an inventory of many small real world examples. Start with the simple Iris Data Set. 

Learn to explore the data and try the following with the tool of choice. Preparing data for data science problems is an art of its own right. Below are the list of techniques you should try your hands at.

  1. Wrangle

    Start by dicing the data into subsets. Understand the variables and their types. Take a look at the variables that might impact the machine learning problem at hand.

  2. Transform

    Try simple data transformations like aggregation, decomposition (splitting the variables) , log transforms.

  3. Visualize

    A key part of solving data problems is to understand the data at hand. Visualization is a wonderful way to understand the data and the hidden gold in them.

  4. Question:

    Majority of the data science problems is to look for answers. Practice asking questions and look for answers in the data.

Applied Data Science Process

Understand the process behind solutions to data science problems.The most common approach to solving data science problems is as follows.

  1. Define the problem: Understand the problem that is being solved
  2. Analyze data: Analyze the data to for patterns and information that could be used to develop a model.
  3. Data preparation:  Prepare the data for modelling.
  4. Model: Start applying machine learning algorithms and validate.
  5. Evaluate:  Evaluate the performance of the model and choose the best performing model.
  6. Deploy: Implement the model in production.

Practice, Practice, Practice

Once you start learn the tools, get your hands at the data ,  practice the applied data science process, it is important to rinse and repeat this process on different datasets across different domains.

Diving Deep

As you start learning the tricks of the trade, it is important to get deep down to the details. The next step is to dive deeper into the algorithms and to understand why they work and how they work. Understand when one is better than the other, under what circumstances they perform better.

Summary

In this post you will learn a step by step approach to learn data science, understand simple approaches to learn and get better at doing applied data science.

 

 

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Amazing list of Data Science Courses

Data science and Analytics are becoming more popular with companies,colleges and people. Many organizations, universities, come are starting to offer data science courses to help people learn data science. Have put together a list of data science courses in no particular order that would be of interest to you. While this is not an exhaustive list and would keep growing , please feel free to comment if your favorite courses are missing from the list. Happy learning!!

  1. Machine Learning specialization
    • Machine Learning Foundations: A Case Study Approach
    • Machine Learning: Regression
    • Machine Learning: Classification
    • Machine Learning: Clustering & Retrieval
    • Machine Learning: Recommender Systems & Dimensionality Reduction
    • Machine Learning Capstone: An Intelligent Application with Deep Learning
  2. Machine Learning for Data Analysis
  3. Practical Data Science
  4. Data Science A-Z: Real-Life Data Science Exercises Include
  5. Machine Learning on Coursera
  6. Analytics Edge
  7. Harvard Data Science Course
  8. Mining Massive Datasets
  9. Making Sense with Data
  10. Data Science Specialization: Introduced by coursera and John Hopkins university is a comprehensive and yet a gentle introduction to the world of data science. The course offers the below topics.
  11. Genomic Data Science specialization: The course covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments.
    • Introduction to Genomic Technologies
    • Genomic Data Science with Galaxy
    • Python for Genomic Data Science
    • Algorithms for DNA Sequencing
    • Command Line Tools for Genomic Data Science
    • Bioconductor for Genomic Data Science
    • Statistics for Genomic Data Science
  12. Intro to Data Science
  13. Introduction to Computational Thinking Data Mitix
  14. Data Analysis and Interpretation Specialization
  15. Executive Data Science Specialization
  16. Applied Data Science with Python
  17. Applied Data Science with R
  18. Data Analysis in Python with Pandas
  19. Introduction to Python for Data Science
  20. Big Data applications and Analytics
  21. Statistics and Data Science in R from Beginner to Advanced
  22. Apache Hadoop – Machine Learning and Hadoop Eco System
  23. Data Analysis and Statistical Inference
  24. Driving Business Results with Big Data
  25. Data Mining specialization
    • Pattern Discovery in Data Mining
    • Text Retrieval and Search Engines
    • Cluster Analysis in Data Mining
    • Text Mining and Analytics
    • Data Visualization
    • Data Mining Capstone
  26. Intro to Hadoop and Mapreduce

Crack Your Next Data Science Interview

Preparing for a data science interview might seem like a huge mountain to climb with a huge variety of topics piled in front . But it isn’t hard as it seems to be.

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The time is now!!

Having a wide range of topics to cover, calls for a need to set aside time and prepare meticulously for topics . Interviews can range from explaining logistic regression to a 5 year old to tuning the parameters of a model. Set aside a time every day to prepare and religiously sit down to prepare for on the topic interview.  With consistent effort it is easier to be there on top of the mountain. From experience below are the topics we should be covering to ace your next data science interview

With a wide variety of topic it is entirely possible to get sucked into one of these holes. This makes it necessary to fix SMART goals and prepare towards these goals.

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Below are the steps which I personally followed to prepare for my interviews.

  1. Review your background and prepare a list of topics you may want to cover. As data scientist come from different backgrounds such as political sciences, statistics, software engineering. It is important to understand your weak links and to prepare towards strengthening it.
  2. Write down your goals and prepare a schedule to work on the small weak links. By writing your goals you create a subconscious wiring to work towards these goals.
  3. Make a commitment by setting a time aside every day for you to religiously study the topics on your weak links list.
  4. Attend Interviews: Attending interviews is another way to get feedback to understand your week links and iterate over them.
  5. Review your goals: Set weekly review meetings with your self to review your current preparation

While these steps are important below are the topics which are essential for a data scientist to know.

Basic Mathematics

To become a good data scientist one must have the ability to deliver insights from the data. You would be able to deliver insights with descent  understanding of mathematical concepts. Go through a refreshers of linear algebra, probability and statistics theory.

Asking the right questions

This is more learned by practiced than taught. Many employers look in for the curiosity and the ability of the candidate to ask questions that can extract insights from the data. Take up a totally unknown data set and practice asking questions and look for answers for your question. With this approach you would improve your questions and strengthen your abilities to find the answers.

Applied machine learning

It is important to understand the basic algorithms in machine learning. Interviewers focus on how the candidate formulates the problem and his ability to transform business into an analytical problem. If you are new to machine learning, a good place to start understanding these concepts would be to enroll in a course or learn from the web. Do check the data science specialization at Coursera and nano degree’s at Udacity. These are a great place to start.

Learn white board coding

This is similar to a software engineer position where the interviewers test the candidate’s ability to define, analyze, solve and test the problem at hand. It is important to brush up concepts of algorithms and data structure. This has been a part of many product oriented data science interviews where the data scientist are expected to be good programmers. There are tons of websites and books to get you started here.

Get the right tools

Thou there many a wide range of tools to express analytics, the top choice of many data scientists have been python and R. Both the languages have great machine learning libraries. These tools would be good to know and have in your toolbox.

 Be a data hacker

Learn data wrangling and mugging techniques in the language of choice. This helps to get up to speed with any given data set.

 Understand databases

Relational databases are a part of every industry and it is important to learn the basics of databases and how to write efficient queries.

 Learn Data Visualization

The best way to start understanding the data is to visualizing. Choose and learn visualization techniques in a tool of choice. Thou it would not be asked during an interview but it is a must required skillset for a good data scientist.

Practice

Practicing the theoretical concepts you learn with help you develop a better understanding of the concepts and also understand your weakness quickly.

Research about the role

Along with preparing for the interview, it is essential to align your skills to the type of data science role you are looking for.  Think about what kind of data scientist you would want to be and which type of teams you would like to be a part of. Ask appropriate questions to understand the requirements of the role and tailor your needs. Look up the profiles of the people who would be interviewing  to understand their background and performing similar roles at the company. This would help you to be understand the type of questions you could expect during the interview. It is important to identify the type of role the employer is looking to fill in, and focus your preparation towards that direction. Take time to understand the job description and also the background of people who would be interviewing you. Remember to work on your weakness on the chosen type of roles. Below are the simplified types of data scientist employers commonly look for.

Business Savvy Data Scientist

The business savvy data scientist focusses on building analytic solutions to help business users and final decision makers.  They help to understand the underlying problems of a company’s marketing campaign, to understand churn or what interest the customers. Communication and story telling plays a major role for these type of roles as it involves communicating the value to non-technical people. They do not have to build complex models, but must unearth the value from the data to answer the questions of why and how.

Product Savvy Data Scientist

The other type of data scientist focuses on building products to help businesses. They build high complex models using sophisticated statistical and machine learning algorithms. They are very focused on improving the performance of the models where it has direct impact on the company’s product. They require to posses good statistical and solid computer science skills.

Hope the above steps helps you to crack your next data science interview. Don’t wait to make your next leap.

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Resources to get Started