How to Improve Data Science Skills

Datanscience is a collective pool of various algorithms, tools, machine learningnprinciples that work in unison to extract hidden patterns from raw data. Itnrequires a diverse set of skills and demands knowledge from aspects ofnmathematics, science, communication, and business. Honing a diverse skill set,ndata scientists gain the ability to analyze numbers and influence decisions.

The core objective of data scientists lay in bridging the gap between numbers and actions by using information to affect real world decisions. This demands excellent communication skills along with understanding the implications of their analysis and recommendations to businesses.

Data science skills are not confined to data, statistics and pre-defined tools, it goes far more beyond to simplifying information for people in a way that it could be utilized for decision-making effectiveness.

Moreover,na data scientist may be an opportune for a wide variety of roles and includes anvariety of positions which mandates the different skill sets based on thenindustry.

Letnus have a look at the technical as well as non-technical skills that can help improvenyour data science skills.

Non-TechnicalnSkills Required By Data Scientists

Herenare a few non-technical skills that a data scientist must possess which arenmostly overlooked but play an important role in applying the technical skillsnright to the job.

• EFFECTIVE COMMUNICATION

The most vital of the skills a data scientist must possess is comprehension and communication. The data language isn’t understood by all so it is necessary that data scientists have the skill of communicating the technical findings in a simplified manner to their non-technical colleagues or the senior management in the board meetings.

Narratena story with the data that gives a compelling message to the onlookers.

Thoughndata cleaning, wrangling, processing and analysing are important steps in datanscience, all of them do not carry much worth without effective communication.nIn order to communicate, one must be able to visualize in the first place. Thenart of visualization leads the data scientist to craft an influential story fromndata. Humans inherently understand and get impacted by visuals than numbers.nSo, creatively presenting a piece of information and communicating it furthernis of absolute importance that too in a way that is understood by the audience.

• BUSINESS ACUMEN

Datanscientists are looked upon as profound resources of data analysis andnpredictions. Unlike before, data scientists are required in almost everynindustry and with the growing amount of data, its applications are alsontremendously increasing.

Every industry is different and has diverse goals and unique datasets. In order to apply data science skills accurately to a specific industry, a data scientist needs to have a clear understanding of the business functions and must possess the ability to interpret business implications of their data insights. Some industries have a unique vocabulary and terms that need to be studied by the data scientist first to exhibit the data in a useable manner. Although metrics such as revenue and costs are common across industries, there are some specific KPIs (key performance indicators) that are industry-specific. Without a thorough understanding of the industry, it’s unique goals and limitations, it would be almost impossible to get the right insights and make useful recommendations to management.

Consideringnthe fact that a data scientist has a pre-defined job title, roles and set ofnresponsibilities, it must also be added that he/she must have an in-depthnunderstanding of the respective industry as the data scientist’s tasks wouldnvary greatly depending on the industry.

• PROBLEM SOLVING SKILLS

Datanscientists are ultimate problem solvers. With data-driven problem-solvingnskills, they excel in presenting problems in a way that triggers decisionnmaking. With the help of a structured approach in framing and identifyingnproblem areas, data scientists help simplify and speed up the decision-makingnprocess.

Annunderlying expectation from a data scientist is to know how to approach anproblem area productively. With the vision of a data expert, it is easier tonidentify the features of a situation and intelligently tap the areas to investigatento yield the desired answer. Additionally, they are deemed to know which datanscience methods to apply to specific problems. Apart from understanding machinenlearning and statistics, one must know how to integrate the availableninformation with the business’ goals while deciding ways of solving problems.

One can not anticipate data science problems nor there is a full-proof solution for every problem. It is likely for decision makers to get overwhelmed by the options to explore. Here comes the role of a data scientist who can exactly gauge a probably right track and also manage the progress. Data science is a pool of techniques such as Six Sigma that enable data scientists to solve real world data science problems in a structured manner.

Technical Skills Required By A DatanScientist

Technicalnskills are what set the data scientists apart. These include knowledge ofnspecialized tools and programming languages that are used by specific businesses.nHowever, there are a list of technical skills that ideally every data scientistnmust possess. Usually, data scientists use data mining, machine learning andnartificial intelligence with certain programming languages to apply variousndata analysing tools. In addition, they must also have a brief understanding ofnthe software engineering principles in order to integrate the tools andndifferent programming languages that they would need to use.

• DATA VISUALIZATION

Probablyna major responsibility of a data scientist is to make data as presentable asnpossible for users to get better insights of raw data and to derive the desiredninformation out of it. Visualizations are important in the first place becausenthey guide the thought process of people viewing it for further analysis. Theynare used to create impactful data stories that communicate an entire set ofninformation in a systematic format so that the audiences are able to extractnmeaning out of it and detect problem areas in order to propose solutions.

Withoutndata visualization tools, it would be practically impossible to implementnchange or cater to the desired problems. Today, there are many datanvisualization tools to select from. In most of the programming languages,nyou’ll find libraries that enable visualization of data. In JavaScript, datancan be visualized using the D3.js visualization library, Python uses Matplotlibnand pandas while R offers many data visualization tools including ggplot2.nTableau is the most trending, high-level platform that offers amazing datanvisualization options extracting data from many different sources.

• DATA WRANGLING

Often the data comes from a variety of sources and needs remodelling to be able tonderive informational insights. It is important to make the data free fromnimperfections such as inconsistent formatting, missing values etc. Datanwrangling allows you bring the data on a uniform level that can be furthernprocessed easily. Obviously, for a data scientist to use data to their best, itnis important to possess the knowledge of organizing clean data from thenunmanageable raw data.

• PROGRAMMING LANGUAGES & SOFTWARE

Data scientists deal with raw data that comes from a variety of sources and in different formats. Such data is filled with misspellings, duplications, misinformation and incorrect formats that can mislead your results. To correctly present the data, it is important to extract the data, clean it, analyze and visualize it. Below are six broadly used tools that are recommended strongly for data scientists:

1. R: R is a programming language that is widely used for data visualization, statistical analysis and predictive modelling. It has been around since many years and has been contributing largely to data analysts with its huge network (CRAN) that provides a complete package to allow analysts to perform various data-related tasks.
2. Python: Python initially was not looked upon as a data analytics tool. The pandas python library enables vectorized processing operations and efficient data storage. This high-level programming language is fast, user-friendly, easy to learn and powerful. It has been used for general programming purposes for long now and therefore allows easy merger of general-purpose code and Python data processing.
3. Tableau: Lately emerged as an amazing data visualization tool, Tableau, a Seattle-based software company offers an exclusive suite of high-end products that surpass the science resources such as R and Python. Although Tableau lacks the ultimate efficiency in reshaping and cleaning data and doesn’t provide options for procedural computations or offline algorithms, it is increasingly becoming a popular tool for data analysis and visualizations due to its highly interactive interface and efficiency in creating beautiful, dynamic dashboards.
4. SQL: Structured Query Language (SQL) is a special purpose programming language that allows for extracting and curing data that is held in relational database management systems. SQL allows users to write queries, insert data, update, modify and delete data. Though all of these can also be done using R and Python, writing an SQL code derives more efficient output and provides reproducible scripts.
5. Hadoop: Hadoop, an open source software framework fosters distributed processing of large amounts of data sets using simple algorithms from large clusters of computers. Hadoop is largely used in industries due to its immense computing power, fault tolerance, flexibility and scalability. It enables programming models such as MapReduce that enables processing of vast amounts of data.

• STATISTICS

Thoughnthere are many automated statistical tests embedded within software, a datanscientist needs to possess a rational statistical sensibility to apply the mostnrelevant test for performing result-oriented interpretations. A solid knowledgenof linear algebra and multivariable calculus assist data scientists in buildingnanalysis routines as needed.

Datanscientists are expected to understand linear regression, exponential andnlogarithmic relationships while also knowing how to use complex techniques suchnas neural networks. Most of the statistical functions are done by computers in minutes, however, understanding the basics is essential in order to extract thenfull potential. A major task of data scientists lay in deriving the desired outputnfrom computers and this can be done by posing right questions and learning hownto make computers answer them. Computer science is backed in many ways bynmathematics and therefore data scientists need to have a clear understanding ofnmathematical functions to be able to efficiently write codes to make computersndo their job perfectly.

• ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

AI is the most trending topics today. It empowers machines by providing intelligence in the real sense to minimize manual intervention to extreme levels. Machine learning works on algorithms that are automated to obtain rules and analyse data and is largely used in search engine optimizations, data mining, medical diagnosis, market analysis and many other areas. Understanding the concepts of AI & machine learning play a vital role in learning industry needs and therefore are at the forefront of data science skills that a data scientist must possess.

• MICROSOFT EXCEL

Eve before any of the modern data analysis tools existed, MS-Excel had been there.nIt is probably the oldest and most popular data tools.

Although now there are multiple options to replace MS-Excel, it has been proven thatnExcel offers some really surprising benefits over others. It allows you to namen& create ranges, sort/filter/manage data, create pivot charts, clean datanand look up for certain data among millions of records. So, even though younmight feel that MS-Excel is outdated, let me tell you it is absolutely not.Non-technical people still prefer using Excel as their only source of storingnand managing data. It is an important pre-requisite for data scientists to havenan in-depth understanding of Microsoft Excel to be able to connect to the datansource and efficiently pick data in the desired format.

Datanscientist are greatly in demand with the growing amounts of data every secondnand it poses an alluring career path for people who love to work with data.nHowever, the world is already aware of the huge potential of data science andnare crowding up in the marketplace. It is important to upgrade yourself withnthe necessary skill sets to ensure you don’t lose the race. Yu don’t have tonsearch any farther. Vinsys offers a wide pool of trainings to enable you upliftnyour career path now. With Vinsys, you get to learn from highly experiencednindustry professionals and secure a promising addition to your profile fornunlocking many career opportunities at very reasonable prices.