Practical Manual for Data Scientists
With the current boom for Data Science and the growing demand, becoming a Data Scientist is a dream of many people. So if you want to make a career change and emerge as a data scientist, now is the time. Here are the first steps to give you an overview of what you might expect.
How to become a Data Scientist
Now let’s start with simple questions which will give you an overview who is a Data Scientist, what he or she does, what skills this job requires and what a career path may look like.
Who is a Data Scientist?
A data scientist is a professional with a deep understanding of information, algorithms and data visualization. To be a data scientist, you want to possess the capability to be a part of a team, apprehend information structure, examine information, layout and create charts and graphs, and write concise code for practical tasks.
There are different types of Data Science Jobs:
- Data Analyst — mostly working with data cleaning and pre-processing
- Data Scientist — both cleaning data and building models with them to predict, analyze and extract insights
- Machine Learning engineer — builds machine learning models on company’s data
In real life you’ll find job offers for any of those positions called mostly a ‘Data Scientist offer’. That’s why it’s important you read the description and requirements to get to know what’s involved really.
What is a Data Scientist Salary and why this much?
A Data Scientist’s salary relies upon on their precise role, but they generally work within the discipline of analytics or system learning, often running with huge statistics sets. That’s why they skills are crucial for the success of any company and they tend to earn over $100,000 a year (we’re talking about the US here, but it is similar in other developed countries).
Data Scientists need to have extraordinary analytical capabilities, revel in in programming or databases, and strong writing abilties.
A Data Scientist’s process is to develop and examine information, and then examine that facts to create insight. These insights may be used in plenty of ways, such as in diverse business selections, and are regularly used to make recommendations or to help make a enterprise case for a brand new product or service. Data scientists work across quite a few different information technology topics such as:
- business intelligence,
- web analytics,
- natural language processing,
- social media analysis,
- predictive analytics,
- system learning,
- data mining
You can discover a listing of some of the important job positions and a way to apply to these positions on LinkedIn or AngelList or numerous other web sites which allow you to browse options in your area. Go ahead and check them!
What talents you’ll want to grow to be a Data Scientist
Data Scientists are in great demand, so you’ll want to have a well-rounded skill set if you’re inquisitive about this kind of task.
Some of the capabilities you’ll need include:
- Excellent analytical abilities, including the potential to apprehend facts
- Good challenge management capabilities, which includes the capacity to plot and manage projects and speak with others
- Good conversation talents, which include the capacity to write truly and concisely
- An understanding of the importance of statistics integrity and privateness issues, as well as the significance of knowing your users
- Excellent quantitative capabilities, consisting of the ability to use data to advantage insight and communicate the results simply
- Ability to apprehend complex records and interpret results
- The ability to think critically and problem solve, both in terms of understanding the big picture and in terms of making specific decisions on how to solve a particular problem
Technical skills required of Data Scientist
From more technical standpoint you will also need a number of the following — in particular if you’re applying for non-junior positions:
- Expertise in Python (sometimes also Excel, R or SAS)
- Experience in machine learning
- Experience with databases (e.g., relational databases or NoSQL databases)
- Experience in data visualization
- Ability to learn quickly
In addition, facts scientists typically work in teams, this means that you’ll be requested to study a lot of things quickly.
It’s not all about data, but rather information, so you’ll need to be an active learner if you want to develop the capabilities vital to be successful in this industry.
3 steps to turn out to be a Data Scientist
To finish this manual with something practical there are 3 steps for you to try in order to become a Data Scientist and have a fantastic career ahead of you.
- Build your repository on GitHub and start an open-source project. You can take a dataset from Kaggle and build something round that. Usually classification issues have a tendency to be easier. This will permit you to hone your talents and show a potential enterprise your engagement.
- Engage in Facebook/LinkedIn groups about Data Science and Machine Learning. Try locating meetups and conferences close to you and attend them to meet more experience people. It’s always right to have someone to guide you.
- Write extra code! Data Science is a practical talent in the end. Share it to your social media — update your LinkedIn profile to have higher chances to find a job.
And remember it’s all about patience.
Data Science is a marathon.
You won’t get there in 2 weeks nor 2 months (unless you have prior vast experience with coding). That’s why you should take your time, learn everything from scratch and really go into details with your training.
Finally, if you want to have an overview of what it means to be a Data Scientist, then have a look at my book Data Science Job: How to become a Data Scientist which will guide you through the process.
And if you want to know even more, read my other articles about becoming a Data Scientist: