AI News, How can I become a data scientist?
- On Monday, June 4, 2018
- By Read More
How can I become a data scientist?
I’m going to excerpt out a guide to data science jobs I created, and specifically a section that talks about the skills and tools you need, as well as the resources needed to become a data scientist.
Full disclosure: I work for a company that helps people break into a data science career with a full flexible, online data science bootcamp featuring personalized mentoring from experts, and career coaching.
Statistics How to become a data scientist with statistics You must know statistics to infer insights from smaller data sets onto larger populations.
To understand data science you must know the basics of hypothesis testing, and design experiments to understand the meaning and context of your data.
Algorithms How to become a data scientist with algorithms Algorithms are the ability to make computers follow a certain set of rules or patterns.
Understanding how to use machines to do your work is essential to processing and analyzing data sets too large for the human mind to process.
In order for you to do any heavy lifting in data science, you’ll have to understand the theory behind algorithm selection and optimization.
You’ll have to decide whether or not your problem demands a regression analysis, or an algorithm that helps classify different data points into defined categories.
According to 3M and Zabisco, almost 90% of the information transmitted to your brain is visual in nature, and visuals are processed 60,000 times faster than text.
Data visualization is the art of presenting information through charts and other visual tools, so that the audience can easily interpret the data and draw insights from it.
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.
Domain Expertise How to become a data scientist with domain expertise As a data scientist, you should know the business you work for and the industry it lives in.
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).
Introduction to Excel 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 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.
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.
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.
How to become a data scientist with Hadoop Hadoop is an open-source ecosystem of tools that allow you to MapReduce your data and store enormous datasets on different servers.
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.
If you’re interested in a mentored data science bootcamp that will help guide you along the steps you need to become a data scientist, check out Springboard’s Data Science Career Track!
How To Ace Data Science Interviews: R Python
A big part of data scientists’ day-to-day involves manipulating, analyzing, and visualizing data in an interactive programming environment.
In my view, Python’s strength for data science is in its ability to serve as a real backend language for production systems, meaning any modeling you do as a data scientist can potentially be implemented with little effort on a live website or software product.
While R supports all the standard CS data structures and techniques such as arrays and for loops, it really excels when you’re working on a rectangular data set, like you’d see in a typical spreadsheet program.
Unlike a spreadsheet though, you can still take advantage of computer science concepts like iteration and abstraction (more on these below) which makes it orders of magnitude more powerful than something like Excel.
Additionally, R is the de facto language for quantitative researchers in academia, meaning that the most cutting edge statistical techniques are often available as R packages long before they make their way to any other place, including Python.
So if your primary workflow involves doing offline analysis and data visualization, and especially if you want access to state-of-the-art statistical packages, R is where you want to be.
But again, you can’t really go wrong — both are amazingly useful, and chances are you can make either language do whatever data science task you’re trying to accomplish.
It manages packages, provides access to help files, displays visualizations, and gives you a nice, customizable text editor along with your console.
Instead of a local IDE, Jupyter provides a browser-based notebook, that lets you separate your code into executable chunks, so you run each piece of code and analysis one at a time.
Iteration is an important concept in computer science and is deeply connected to data structures— essentially it’s a way to perform an operation on each item within a data structure.
This might seem like a lot, but once you learn the concept you’ll find that all these different options are just various ways of applying the same fundamental concept: take a data structure and do something with each of it’s elements.
While this example might not save much time or code, functions can get much more complex, at which point writing a function for repetitive tasks can make you code much more readable and concise.
In Python this means getting to know Pandas, a package that provides an entire framework for operating on data frames, rectangular data sets with rows and columns.
While R has native support for rectangular data sets in the form of matrices and data frames, you’ll still make your life much easier by learning either dplyr or data.table.
Each of these packages provides a great interface for manipulating data frames that is better than base R: dplyr is far more intuitive and readable, while data.table is faster and has more concise syntax.
As with SQL, help out your interviewer by talking through your code as you write, so they know what you’re thinking and can give partial credit in the case that you don’t quite get to a final answer.
For example, a simple linear regression that might take you hours if you were to code it from scratch can be executed with just: In Python, you’ll need at least the Numpy and Scipy packages to make sure you have your basic statistical functions covered, but like R, once those are installed you’ll be good to go.
Besides whiteboarding questions, lots of data science interviews will have a takehome component, that asks you to take a sample data set, analyze it and draw some conclusions.
few tips for visualizations in a take home assignment: title your graphs, zero and label your axes, include error bars if applicable, and pick a few colors and use them consistently.
But really once you know the basics of your preferred programming language, it’s all about getting comfortable with a few key tools: data manipulation, statistics, and visualization.
24 Ultimate Data Science Projects To Boost Your Knowledge and Skills (& can be accessed freely)
This article was originally published on October 26, 2016 and updated with new projects on 30th May, 2018.
Nowadays, recruiters evaluate a candidate’s potential by his/her work and don’t put a lot of emphasis on certifications.
We believe everyone must learn to smartly work with huge amounts of data, hence large datasets are included.
To help you decide where to begin, we’ve divided this list into 3 levels, namely:
Nothing could be simpler than the Iris dataset to learn classification techniques. If you are totally new to data science, this is your start line.
This dataset provides you a taste of working on data sets from insurance companies –
Thus, it’s a fairly small data set where you can attempt any technique without worrying about your laptop’s memory being overused.
This dataset is specific to time series and the challenge here is to forecast traffic on a mode of transportation.
This is a fairly straightforward problem and is ideal for people starting off with data science.
It is a regression problem. The dataset has 25,000 rows and 3 columns (index, height and weight).
It’s a classic dataset to explore and expand your feature engineering skills and day to day understanding from multiple shopping experiences.
This data set is collected from recordings of 30 human subjects captured via smartphones enabled with embedded inertial sensors.
The data comprises of aviation safety reports describing problem(s) which occurred in certain flights.
This dataset comes from a bike sharing service in the United States. This dataset requires you to exercise your pro data munging skills.
You know, machine learning is being extensively used to solve imbalanced problems such as cancer detection, fraud detection etc.
If you want to carve a niche for yourself in this area, you will have fun working on the challenge this dataset poses.
It’s a digit recognition problem. This data set has 7,000 images of 28 X 28 size, totalling 31MB.
When you start your machine learning journey, you go with simple machine learning problems like titanic survival prediction.
Hence, this practice problem is meant to introduce you to audio processing in the usual classification scenario.
This dataset consists of 8,732 sound excerpts of urban sounds from 10 classes.
Audio processing is rapidly becoming an important field in deep learning hence here’s another challenging problem.
This dataset is for large-scale speaker identification and contains words spoken by celebrities, extracted from YouTube videos. It’s an intriguing use case for isolating and identifying speech recognition.
ImageNet offers variety of problems which encompasses object detection, localization, classification and screen parsing.
Companies no longer prefer to work on samples when they the computational power to work on the full dataset.
This dataset provides you a much needed hands-on experience of handling large data sets on your local machines.
The dataset contains thousands of images of Indian actors and your task is to identify their age. All the images are manually selected and cropped from the video frames resulting in a high degree of variability interms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup.
This is an advanced recommendation system challenge. In this practice problem, you are given the data of programmers and questions that they have previously solved, along with the time that they took to solve that particular question.
As a data scientist, the model you build will help online judges to decide the next level of questions to recommend to a user.
The dataset has 265,016 images, 3 questions per image and 10 ground truth answers per question.
Lots of recruiters these days hire candidates by checking their GitHub profiles. Your motive shouldn’t be to do all the projects, but to pick out selected ones based on the problem to be solved, domain and the dataset size.
Specifically, myself and my team have worked with industry leaders to identify a core set of eight data science competencies you should develop.
Programming SkillsNo matter what type of company or role you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade.
This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren’t) a valid approach.
Statistics is important at all company types, but especially data-driven companies where stakeholders will depend on your help to make decisions and design / evaluate experiments.
Machine LearningIf you’re at a large company with huge amounts of data, or working at a company where the product itself is especially data-driven (e.g.
Linear AlgebraUnderstanding these concepts is most important at companies where the product is defined by the data, and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company.
This will be most important at small companies where you’re an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have.
CommunicationVisualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions.
It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information.
At some point during the interview process, you’ll probably be asked about some high level problem—for example, about a test the company may want to run, or a data-driven product it may want to develop.
- On Monday, June 1, 2020
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