Skills needed to become a Data Scientist


“A data scientist is someone who is better at statistics than any software engineer and better at
software engineering than any statistician”.
Found at the cross section of business and information technology, a data scientist is a professional
with the capabilities to gather large amounts of data to analyze and synthesize the information into
actionable plans for companies and other organizations.
We’ve listed a breakdown of the data scientist skills that are essential for career aspirants in the realm of data and
analytics management.

Business skills:
- Interpersonal skills and superior communication
- Ability to meet deadlines and manage project delivery
- Excellent report-writing and presentation skills
- Critical thinking and problem-solving capabilities

Technical skills:

  • Proficiency with the programming languages of R, SAS, Python, MatLab, and Java:  You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL.

  •  Machine learning: You should be able to explain K-nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python. These algorithms show to employers that you have exposure to how data science can be used in more practical manners.

  • Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision- making process and to design experiments.

  • Data Wrangling: You should be able to clean up data. This basically means understanding that "California" and "CA" are the same thing - a negative number cannot exist in a dataset that describes population. It is all about identifying corrupt (or impure) data and correcting/deleting them.

  • Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them)

Additionally you should also know about -
  • Database systems of SQL and NoSQL
  • Data analytics and data visualization tools, such as Tableau, Qlikview, and D3
  • Operating systems, especially UNIX/LINUX
  • Big data tools, such as Hadoop, Spark, Hive, and Cassandra
  • Understanding of cloud platforms, such as Azure, IBM, and Google
  • Data mining and data cleansing techniques
  • Data modeling and data architecture


In each field, mentioned some buzzwords you should know about. As there are hundreds of resources
online but its highly unlikely that they are personalized to understand the individual queries faced by
career aspirants in the domian of Data Science.

Comments

Post a Comment

Popular posts from this blog

Application of Big Data in Decision Making

Image
Data driven decision making is one of the most trending optics of interest & focus in business organizations; and multi-national corporates are investing big-time in devising their information infrastructure so as to store & process transaction data; and massage them in a way so as to extract valuable business insights from the underlying data structure. Before coming to the application of Big Data in decision making ; it is imperative to understand the concept of Big Data, and how are organization leveraging this data to derive insightful inferences on business & market prospects. What is Big Data? How relevant is it for businesses? The term ‘Big Data’ refers to exceedingly large datasets which can be computationally analyzed to derive relevant trends, patterns & interactions – which may in turn enable valuable descriptive analytics (i.e. a picture of the past & present) as well as predictive analytics (i.e. a picture of the foreseeable future). Th
Read more

Difference between Artificial Intelligence & Machine Learning

Image
Artificial Intelligence & Machine Learning , at times, are used interchangeably by users & developers. Although there is a fair amount of correlation between the two; still the terms & its concepts are not similar to each other. In actual sense, artificial intelligence is a super-set of machine learning as illustrated in the figure below. Generalizing the different branches of artificial intelligence into more fundamental categorization; artificial intelligence can either be general or applied . In current world, applied artificial intelligence is ore prevalent, and is extensively used in designing smart systems. A few examples of applied AI include intelligent stock trading software, automatic vehicles etc. General AI is the current buzzword; and aims to equip systems and machines to perform virtually everything that a human does (and even beyond it). The rise of machine learning is a direct consequence of the interest & investment that has gone in the gener
Read more

How a fresher can enhance data analytics skills?

Image
Data analysts   are required to possess sharp technical skills complimented by strong industry knowledge. They require a complete understanding of relationships that exist among the organization’s various databases and data sources to be effective. As they acquire information using complex query statements and advanced database tools and techniques. I've listed the skills a Data analysts are required to possess and the way an individual can go about enhancing them. 1. Machine learning Machine learning combines data science, math, and software engineering, so it requires an extensive skill set to learn. Machine learning skills include computer science fundamentals, programming, probability and statistics, data modeling and evaluation, algorithms and libraries, and software engineering and system design. Resources to learn machine learning:  Kaggle   has a community of data scientists and machine learning engineers who work together to publish datasets, build mode
Read more