It’s important to know the steps how any one become a data scientist .If you are interested to become a data scientist then it’ll be hopefully help you though tons of articles are on the web.

Business Applications are now in matured stage. Every day man, machine, IoT devices, click stream generating data. The interesting part is to get knowledge and insight from the data. Insight knowledge can give us idea to go ahead in every sector health, biological research, Business opportunity ,agriculture, traffic system  every where . Simple known example is ”People You May Know” in LinkedIn or Facebook, harness the power of data to create value for users.

Data is big business, with companies like Google, Facebook and LinkedIn - and possibly every other corporate body you’ve heard of - creating huge profits out of the way they use data. So, what is a data scientist?

First we try to figure out the definition of Data Science.

It'll give us idea what is Data science ?

After that we assess the skill set required .

Finally, we need to effectively take initiative on earning skill set.

We’re going to know some Data Science definition from Renowned Data Scientist.

“A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.”- Hilary Mason, chief scientist at Bitly

“Google, more than any other company, has pushed the boundaries of what is possible with big data“-Tim O’Reilly, who calls Larry Page - CEO of Google - the world’s top data scientist.

“Transforming business questions into data questions, solving data questions to find data answers, then transforming data answers back into business answers.”- Renee Teate Data Scientist

“A data scientist is someone who can obtain, scrub, explore, model and interpret data, blending hacking, statistics and machine learning.”- Daniel Tunkelang Principal Data Scientist at LinkedIn

“A data scientist is someone who blends, math, algorithms, and an understanding of human behavior with the ability to hack systems together to get answers to interesting human questions from data” -Hilary Mason, chief scientist at bitly

“Data scientists are part digital trends potter and part storyteller stitching various pieces of information together.” -Anjul Bhambhri

““A data scientist… [is] someone who can bridge the raw data and the analysis — and make it accessible. It’s a democratizing role.”-Simon Rogers

“A data scientist is someone who blends, math, algorithms, and an understanding of human behavior with the ability to hack systems together to get answers to interesting human questions from data“-Jake Porway, Former Data Scientist at New York Times

More ..

It’s important to know what skill set is required as a data scientist. Look at job listings online give you idea to select learning track. Few question and answer make you understand about role you have to play with skill and working environment.

What technical stack does the data science team use?

If some form of SQL is mentioned with strong product analysts. If dash boarding packages like Tableau , you can anticipate BI responsibilities. Python and R imply a stronger technical skill set, and if packages like Tensor Flow are mentioned you should definitely be familiar with machine learning.

What department in the company houses the data science team?

Data science teams under Engineering or Operations are often very technical, while those under Finance may have more business intelligence responsibilities. Under product, a data science team may focus more on product analytics and partner with product managers.

What Does the Company Need?

Some employers may have a very clear idea of their data science needs, while others may not. Here are some probing questions you can ask a potential employer to ensure that you fully understand the expectations of your role.

As a data scientist, would I like to be responsible for building and owning data pipelines and ensuring that data is available, reliable, and easily query able?

This is a data engineering function. It is fundamental to the work of other data scientists at a company. These pipelines become the foundation for BI reporting, machine learning models, product analytics etc. If data engineering work is not done properly, analytics work is done slowly or (far worse) unreliably due to the weakness of underlying data. This is just a small sample of possible responsibilities under the title of data scientist. A healthy organization should clearly allocate these responsibilities or assign obvious titles (such as BI analyst or data engineer) to help clarify expectations and coordinate work.

Which skill set are require other then technical skill set ?

Data scientist needs to be able to derive robust conclusions from data. But a data scientist also needs to possess creativity and strong communication skills. Creativity drives the process of hypothesis generation, i.e., picking the right problems to solve the will create value for users and drive business decisions. Communication is essential, because data scientists work in horizontal roles and partner with groups across the entire organization. At LinkedIn, data scientists collaborate with every other product group, as well as with sales and finance. Strong communication skills are a must-have.

What are things Data Science cover ?

Data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. In particular, data science also covers
  • data integration
  • distributed architecture
  • automating machine learning
  • data visualization
  • dashboards and BI
  • data engineering
  • deployment in production mode
  • automated, data-driven decisions
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Data scientists add value in at least three ways:
  • The first is by performing offline analysis that informs mission-critical business decisions, e.g., identifying key user segments or activities.
  • The second is by improving products such as search and recommendations that rely on the quality of data and derived data.
  • The third is by creating data products: for example, LinkedIn Skills shows you the top locations, related companies, relevant jobs, and groups where you can interact with like-minded professionals

What are the skill a Data Scientist required ?

There are four fundamental skills that required :

Fundamental languages like R and Python:

These are the languages most important for data management, cleaning, and modeling. More than proficiency at a single language or even a set of languages, data scientists need to be masters at flexibility. They need to be able to migrate languages and jump back and forth between whichever language best helps solve the problem .To that end, you’ll be best served by learning “building block” languages. These include a statistical computing language like R and a general purpose one like Python.Once you’re familiar with both R and Python, you’ll find that learning any new language becomes much easier. Both are currently the foundational blocks of this field, and new languages will almost certainly share characteristics with them.

Core machine learning algorithms:

It’s important to understand how to compare various machine learning algorithms, and to have the ability to choose the correct parameters for models. Some of these base machine learning classifiers include logistic and linear regression, Naive Bayes, random forests, and clusterings such as k-means. But remember that this is a skill that needs to be developed over time rather than learned all at once. Which is to say that as a new entrant, you shouldn’t be focusing too much of your time on machine learning or artificial intelligence. Regression, Naive Bayes, SVM, and random forests are the most basic set and most interviewers want a candidate to know these before anything else. Needs to focus on core skills like evaluating machine learning classifiers and understanding the types of classification errors that are most important to the client. There is, after all, more value in a true cost analysis than in accuracy rates.

Non-technical skills

The one thing that will set a good data scientist apart from a great one is the ability to quickly zero in on the set of questions that will reveal the right answers.It’s a common mistake to assume that technical skills are the biggest drivers of ultimate success. I’d argue that communication skills and problem-solving abilities are perhaps a little more important. These qualities will help you drive impactful results — whether that be increasing revenue for a company, innovating on a product, or disrupting an entire industry. You’ll go from being a SQL monkey to a trusted business partner.

Problem-solving skills

Understand the problem, it’s significance, and what effect/change it will inspire. Figure out where to find the data. If it doesn’t exist in a usable form, figure out how to collect it. This will help engage the client and eventually lead them to implement your recommendations. It also enables you to spot outliers, and ask revealing questions that get at the heart of the problem. Communicate your findings to the stakeholder such that they are able to understand the big picture impact of your solution.

Honing your problem-solving skills

This means translating your client’s needs into a concrete problem, and breaking it down into a series of steps that lead to a solution. Here’s the process I use, but yours might look different.

Translate technical scientific matters into business context.

It is also about making data and insights accessible to non-technical audiences. Most clients do not speak data; they speak revenue, marketing, sales, or product. It’s your job as a data scientist to translate technical scientific matters into business context. Forget everything else you read.

Personally I want to share a video link :  The 5 questions data science answers Hope you enjoy !

If you’re an aspiring data scientist, make these above pillars your mantra, and success will invariably follow. Have a nice Day !