AI News, Becoming a Data Scientist

Becoming a Data Scientist

This blogpost is an excerpt of Springboard's free guide to data science jobs and originally appeared on the Springboard blog.

Most companies depend on their data scientists not just to mine data sets, but also to communicate their results to various stakeholders and present recommendations that can be acted upon.

The best data scientists not only have the ability to work with large, complex data sets, but also understand intricacies of the business or organization they work for.

Having general business knowledge allows them to ask the right questions, and come up with insightful solutions and recommendations that are actually feasible given any constraints that the business might impose.

Beyond having deep knowledge of the company you work for, you’ll also have to understand the field it works in for your business insights to make sense.

What follows is a broad overview of the most popular tools in data science as well as the resources you’ll need to learn them properly if you want to dive deeper.

If you go from the right to a column to the left, you’ll get different data points on the same entity (for example, a person will have a value in the AGE, GENDER, and HEIGHT categories).

Excel allows you to easily manipulate data with what is essentially a What You See Is What You Get editor that allows you to perform equations on data without working in code at all.

Level of Difficulty Beginner Sample Project: Importing a small dataset on the statistics of NBA players and making a simple graph of the top scorers in the league Introduction to SQL: SQL is the most popular programming language to find data.

A versatile programming language built for everything from building websites to gathering data from across the web, Python has many code libraries dedicated to making data science work easier.

is the most popular programming language taught in universities: the community of Python programmers is only going to be larger in the years to come.

The Python community is passionate about teaching Python, and building useful tools that will save you time and allow you to do more with your data.

Many data scientists use Python to solve their problems: 40% of respondents to a definitive data science survey conducted by O’Reilly used Python, which was more than the 36% who used Excel.

Level of Difficulty: Intermediate Sample Project: Using Python to source tweets from celebrities, then doing an analysis of the most frequent words used by applying programming rules.

The community contributes packages that, similar to Python, can extend the core functions of the R codebase so that it can be applied to specific problems such as measuring financial metrics or analyzing climate data.

Any data set that is too large for conventional data tools such as SQL and Excel can be considered big data, according to McKinsey.

Level of Difficulty:Advanced Sample Project: Using Hadoop to store massive datasets that update in real time, such as the number of likes Facebook users generate.

NoSQL includes a host of data storage solutions that separate out huge data sets into manageable chunks.

Often structured in the JSON format popular with web developers, solutions like MongoDB have created databases that can be manipulated like SQL tables, but which can store the data with less structure and density.

Who Uses This: Data engineers and data scientists will use NoSQL for big data sets, often website databases for millions of users.

You can create datasets by taking data from what is called an API or an application programming interface that allows you to take structured data from certain providers.

Springboard has compiled 19 of our favorite public datasets on our blog to help you out in case you ever need good data right away.

You’ll be able to take data from Wikipedia tables, and once you’ve cleaned the data with the beautifulsoup library, you’ll be able to analyze them in-depth.

The Rvest package will allow you to perform basic web scraping, while magrittr will clean and parse the information for you.

Excel allows you to easily clean data with menu functions that can clean duplicate values, filter and sort columns, and delete rows or columns of data.

You can, for example, replace every error value in the dataset with a default value such as zero in one line of code.

Many of the newer R libraries such as reshape2 allow you to play with different data frames and make them fit the criterion you’ve set.

NoSQL allows you the ability to subset large data sets and to change data according to your will, which you can use to clean through your data.

Excel can add columns together, get the averages, and do basic statistical and numerical analysis with pre-built functions.

You’ll be able to build probability distributions, apply a variety of statistical tests to your data, and use standard machine learning and data mining techniques.

Use pivot tables that display your data dynamically, advanced formulas, or macro scripts that allow you to programmatically go through your data.

You can easily build dashboards and dynamic charts that will update as soon as somebody changes the underlying data.

It’s a powerful environment suited to scientific visualization with many packages that specialize in graphical display of results.

The base graphics module allows you to make all of the basic charts and plots you’d like from data matrices.

Now that you’ve gotten an idea of the skills and tools you need to know to get into data science and how to become a data scientist, it’s time to apply that theory to the practice of applying for data science jobs.

He has five projects dealing with healthcare costs, labor markets, energy sustainability, online education, and world economies, fields where there are plenty of data problems to solve.

By using your data science skills, you can show your ability to make a difference, and create the strongest portfolio asset of all: a demonstrated bias to action.

We continue to emphasize that the best job positions are often found by talking to people within the data science community.

There are several kinds of questions that are always asked in a data science interview: your background, coding questions, and applied machine learning questions.

To prepare for the coding questions, you’ll have to treat interviews on data science partly as a software engineering exercise.

Among some of these questions, you’ll see common ones like: Among some of these questions, you’ll see common ones like: The first type of question tests your programming knowledge.

The second type of question tests what you know about data science algorithms, and makes you share your real-life experience with them.

If you can demonstrate how your data science work can help move the needle for your potential employers, you’ll impress them.

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