How to Prepare for Lambda School and any Data Science Program or School

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So, you have been accepted to lambda school data science program and you have a few weeks or months before your school start date.

After completing the pre-course work, you might be wondering if there is a way for you to prepare for lambda school beyond the pre-course work before your start date. And the answer to that question is yes.

In today’s blog, I am going to tell you ways to prepare for lambda school.

Now, why would you want to prepare for lambda school if you are already going to learn the material in lambda school. The purpose of preparation is to make your life easier while you are in lambda school.

The first time you learn something, it might look strange and you may not fully comprehend it. But the second time you learn something, it is easier for you to grasp the content, you will understand it better, everything will begin to click and light bulbs will start going off, and most importantly, you will be ahead of the people who are learning it for the first time.

You can prepare for lambda school data science program by  learning…

1. Python.

Why python? Python and R are the primary programming languages used in data science. Learning python will not only set you up for success in data science, but it will give you the ability to perform other programming tasks beyond data science. Python is not used just for data science work. It is also used for web development, game development, 3d applications and many other things. Python is a general purpose language, so it is an important language to learn.

There are a few ways to learn python:

• You can go to udacity.com and they have free python tutorial course.
• You can go to codecademy.com and they will have their python 3 online course. I personally took the python 3 course from codecademy because I could code while learning and they have different projects for you to work on to practice your python skills.
• You can also study a python textbook called “python data science handbook” https://jakevdp.github.io/PythonDataScienceHandbook/
• Or you can just use W3 school to learn python too

2. Statistics.

The next thing you might consider studying is statistics. Statistics is at the foundation of data science. Having a good statistics background is essential to your success as a data scientists.

Here are a few resources you can use to study statistics:

• Udacity has a free course called statistics and it covers all the basics that you need to know.
• You can also check out Codecademy’s courses on statistics. They have one called statistics with python and another called statistics with R.
• You can also purchase a statistics textbook and start your education that way.

Lambda school teaches you python and statistics, but getting a head start on those subjects is never a bad idea.

3. R Programming Language.

Something else you might consider learning is R. Lambda school actually does not teach R based on what their syllabus shows. R is mostly used by people in academia to program data science work and R is also used in industry. But, R is not as popular as python and R is mostly used for one thing only which is data science work. It is not a general purpose programming language like Python. Learning R will be more beneficial than harmful.

A few places where you can learn R includes:

• Codecademy statistics with R
• You can also take a class on Udacity called data Analysis and visualization by Georgia tech https://www.udacity.com/course/data-analysis-and-visualization–ud404 that teaches R programming language as part of the course. Udacity also has another course called “Data analysis with R.”
• W3 school R lessons is always an option for people that prefer to learn by reading.

4. Structured Language Query (SQL).

The next thing you might want to consider learning to prepare for lambda school data science program is SQL. SQL just like python is essential to your success as a developer and as a data scientist. SQL is how we store and manage data in a relational database. And NoSQL is how we deal with data in a non-relational database. You will use AQL in data science, in data engineering, in software development, and most importantly, it is easy to learn. There are different flavors of SQL, but that is a discussion for a different day.

A few resources that you can use to learn SQL includes:

• Codecademy SQL online courses or their “analyze data” skill path. After I finished my python lesson with codecademy, I enrolled in the “analyze data” skill path so that I can learn SQL and also work on some SQL projects to enhance my SQL skills.
• You can also learn SQL from w3 school.
• You can take a course on udacity called “SQL for data analysis” https://www.udacity.com/course/sql-for-data-analysis–ud198 that teaches you SQL.

In addition to the different ways I have listed for you to prepare, you can also do anything else you feel is necessary to prepare. If you feel like your math skills needs refreshing, you can take classes such as

Linear algebra refresher course by Udacity https://www.udacity.com/course/linear-algebra-refresher-course–ud953

Introduction to discrete mathematics by UC san Diego in Coursera https://www.coursera.org/specializations/discrete-math

You can basically search around on Codecademy, Coursera, Udacity, and W3 school to find different courses in relation to data science that fits your need.

Since there are so many options of things you could possibly learn, what do you choose and where do you start.

I will say to choose and start with your weakest link.

If you know you are really good at math and math concepts are easy for you to understand, I will say start with programming courses. Programming is only mastered through repetition and practice. The more you code, the better you will become at it.

If you are already really good at programming, I will say you start by taking online course in statistics and math. If you are not good at either programming or math and you struggled with math in school, again I will suggest you start by learning statistics and taking algebra math classes.

For me, math is a bit easier for me to comprehend quickly, so I started with programming. First I learned and did projects in python, then I moved on and learned SQL.

My next objective will be to learn statistics with R from codecademy and data analysis with R from Udacity. Based on lambda school curriculum, I can deduce that R is not taught in lambda school and I don’t want to be a complete novice when somebody brings up R in a data science conversation.

Here is a word of advice, whatever you choose, make sure you stick to it until you finish it. Many people have the natural tendency to jump around from course to course and from project to project without getting anything done and without actually learning anything in depth beyond the first couple of lessons.

I hope this helped you prepare for lambda school data science program.

Let me know how you are preparing for Lambda school data science program below.