What is Data Science?

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Data Science continues to be a hot topic among skilled professionals and organizations that are focusing on collecting data and drawing meaningful insights out of it to aid business growth. A lot of data is an asset to any organization, but only if it is processed efficiently. The need for storage grew multifold when we entered the age of big data. Until 2010, the major focus was towards building a state of the art infrastructure to store this valuable data, that would then be accessed and processed to draw business insights. With frameworks like Hadoop that have taken care of the storage part, the focus has now shifted towards processing this data. Let us see what is data science, and how it fits into the current state of big data and businesses. 

Broadly, Data Science can be defined as the study of data, where it comes from, what it represents, and the ways by which it can be transformed into valuable inputs and resources to create business and IT strategies. 

what is data science graphical representation(Source: datascience@berkeley)

 

Why businesses need Data Science?

We have come a long way from working with small sets of structured data to large mines of unstructured and semi-structured data coming in from various sources. The traditional BI tools fall short when it comes to processing this massive pool of unstructured data. Hence, Data Science comes with more advanced tools to work on large volumes of data coming from different types of sources such as financial logs, multimedia files, marketing forms, sensors and instruments, and text files. 

Mentioned below are relevant use-cases which are also the reasons behind Data Science becoming popular among organizations:

– Data Science has myriad applications in predictive analytics. In the specific case of weather forecasting, data is collected from satellites, radars, ships, and aircraft to build models that can forecast weather and also predict impending natural calamities with great precision. This helps in taking appropriate measures at the right time and avoid maximum possible damage. 

– Product recommendations have never been this precise with the traditional models drawing insights out of browsing history, purchase history, and basic demographic factors. With data science, vast volumes and variety of data can train models better and more effectively to show more precise recommendations.

– Data Science also aids in effective decision making. Self-driving or intelligent cars are a classic example. An intelligent vehicle collects data real-time from its surrounding through different sensors like radars, cameras, and lasers to create a visual (map) of their surroundings. Based on this data and advanced machine learning algorithm, it takes crucial driving decisions like turning, stopping, speeding etc. 

 

What is data science

 

Why you should build a career in Data Science? 

Now that we have seen why businesses need data science in the above section, let’s see why is data science a lucrative career option through this video:

 

Who is a Data Scientist?

A data scientist identifies important questions, collects relevant data from various sources, stores and organizes data, decipher useful information, and finally translates it into business solutions and communicate the findings to affect the business positively. 

Apart from building complex quantitative algorithms and synthesizing a large volume of information, the data scientists are also experienced in communication and leadership skills, which are necessary to drive measurable and tangible results to various business stakeholders. 

 

What is the prerequisite skill sets to Data Science?

Data Science is a field of study which is a confluence of mathematical expertise, strong business acumen, and technology skills. These build the foundation of Data Science and require an in-depth understanding of concepts under each domain. The three requisite skills are elaborated below:

Mathematical Expertise: There is a misconception that Data Analysis is all about statistics. There is no doubt that both classical statistics and Bayesian statistics are very crucial to Data Science, but other concepts are also crucial such as quantitative techniques and specifically linear algebra, which is the support system for many inferential techniques and machine learning algorithms. 

Strong Business Acumen: Data Scientists are the source of deriving useful information that is critical to the business, and are also responsible for sharing this knowledge with the concerned teams and individuals to be applied in business solutions. They are critically positioned to contribute to the business strategy as they have the exposure to data like no one else. Hence, data scientists should have a strong business acumen to be able to fulfil their responsibilities. 

Technology Skills: Data Scientists are required to work with complex algorithms and sophisticated tools. They are also expected to code and prototype quick solutions using one or a set of languages from SQL, Python, R, and SAS, and sometimes Java, Scala, Julia and others. Data Scientists should also be able to navigate their way through technical challenges that might arise and avoid any bottlenecks or roadblocks that might occur due to lack of technical soundness.

 

Other roles in the field of data science:

So far, we have understood what is data science, why businesses need data science, who is a data scientist, and what are the critical skill sets that are required to enter the field of data science. Now, let us look at some other data science job roles apart from that of a data scientist:

– Data Analyst: This role serves as a bridge between business analysts and data scientists. They work on specific questions and find results by organizing and analyzing the given data. They translate technical analysis to action items and communicate these results to concerned stakeholders. Along with programming and mathematical skills, they also require data wrangling and data visualization skills. 

– Data Engineer: The role of a data engineer is to manage large amounts of rapidly changing data. They manage data pipelines and infrastructure to transform and transfer data to respective data scientists to work on. They majorly work with Java, Scala, MongoDB, Cassandra DB, and Apache Hadoop. 

 

Data Science Salary trends across job roles:

what is data science

(Source: Analytics India Magazine – Salary Study 2019)

 

Who can become a data scientist/analyst/engineer?

Data Science is a multidisciplinary subject and it is a big misconception that one needs to have a PhD in science or mathematics to become a data science professional. Although a good academic background is a plus when it comes to data science profession, it is certainly not an eligibility criterion. Anyone with a basic educational background and an intellectual curiosity towards the subject matter can become a data scientist. 

 

Critical tools in Data Science Domain:

SAS – It is specifically designed for operations and is a closed source proprietary software used majorly by large organizations to analyze data. It uses the base SAS programming language which is generally used for performing statistical modelling. It also offers various statistical libraries and tools that are used by data scientists for data modelling and organising. 

Apache Spark – This tool is an improved alternative of Hadoop and functions 100 times faster than MapReduce. Spark is designed specifically to manage batch processing and stream processing. Several Machine Learning APIs in Spark help data scientists to make accurate and powerful predictions with given data. It is a highly superior tool than other big-data platforms as it can process real-time data, unlike other analytical tools which are only able to process batches of historical data.

BigML – BigML provides a standardized software using cloud computing, and a fully interactable GUI environment that could be used for processing ML algorithms across various departments of the organization. It is easy to use and allows interactive data visualizations. It also facilitates the export of visual charts to mobile or IoT devices. BigML also comes with various automation methods that aid the tuning of hyperparameter models and help in automating the workflow of reusable scripts. 

D3.js – D3.js is a javascript library that makes it possible for the user to create interactive visualizations and data analysis on their web browser with the help of its several APIs. It can make documents dynamic by allowing updates on the client-side, it actively uses the change in data to reflect visualization on the browser. 

MATLAB – It is a numerical computing environment that can process complex mathematical operations. It has a powerful graphics library to create great visualizations that help aid image and signal processing applications. It is a popular tool among data scientists as it can help with multiple problems ranging from data cleaning and analysis to much advanced deep learning problems. It can be easily integrated with enterprise applications and other embedded systems. 

Tableau – It is a Data Visualization software that helps in creating interactive visualizations with its powerful graphics. It is suited best for the industries working on business intelligence projects. Tableau can easily interface with spreadsheets, databases, and OLAP (Online Analytical Processing) cubes. It sees a great application in visualizing geographical data. 

Matplotlib – Matplotlib is developed for Python and is a plotting and visualization library used for generating graphs with the analyzed data. It is a powerful tool to plot complex graphs by putting together some simple lines of code. The most widely used module of the many matplotlib modules is the Pyplot. It is an open-source module that has a MATLAB-like interface and is a good alternative to MATLAB’s graphics modules. NASA’s data visualizations of Phoenix Spacecraft’s landing were illustrated using Matplotlib.

NLTK – It is a collection of libraries in Python called Natural Language Processing Toolkit. It helps in building the statistical models that along with several algorithms can help machines understand human language. 

Scikit-learn – It is a tool that makes complex ML algorithm simpler to use. A variety of Machine Learning features such as data pre-processing, regression, classification, clustering, etc. are supported by Scikit-learn making it easy to use complex ML algorithms. 

TensorFlow – TensorFlow is again used for Machine Learning, but more advanced algorithms such as deep learning. Due to the high processing ability of TensorFlow, it finds a variety of applications in image classification, speech recognition, drug discovery, etc. 

If you are interested in pursuing a career in Data Science, check out Great Learning’s postgraduate program in Data Science and Business Analytics. The Analytics and Data Science course from Great Learning has been ranked No.1 consistently since 2014 by Analytics India Magazine. The program provides international recognition and dual certificate from the University of Texas at Austin, McCombs School of Business and Great Lakes, India. You will get to learn from the top-ranked data science faculty along with the flexibility to learn at your time and space with the online learning and weekend classes format.

21 Open Source Python Libraries You Should Know About

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The probability that you must have heard of ‘Python’ is outright. Guido Van Rossum’s brainchild – Python, which dates back to the ’80s has become an avid game changer. It is one of the most popular coding languages today and is widely used for a gamut of applications. In this article, we have listed 21 Open Source Python Libraries you should know about.

What is a Library

A library is a collection of pre-combined codes that can be used iteratively to reduce the time required to code. They are particularly useful for accessing the pre-written frequently used codes, instead of writing them from scratch every single time. Similar to the physical libraries, these are a collection of reusable resources, which means every library has a root source. This is the foundation behind the numerous open-source libraries available in Python. 

Let’s Get Started!

1. Scikit- learn: It is a free software machine learning library for the Python programming language and can be effectively used for a variety of applications which include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing.

Scikit-learn requires:

  • Python (>= 2.7 or >= 3.3),
  • NumPy (>= 1.8.2),
  • SciPy (>= 0.13.3).

21 open source python libraries you should know

Spotify uses Scikit-learn for its music recommendations and Evernote for building their classifiers. If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip.

2. NuPIC: The Numenta Platform for Intelligent Computing (NuPIC) is a platform which aims to implement an HTM learning algorithm and make them public source as well. It is the foundation for future machine learning algorithms based on the biology of the neocortex. Click here to check their code on GitHub.

3. Ramp: It is a Python library which is used for rapid prototyping of machine learning models. Ramp provides a simple, declarative syntax for exploring features, algorithms, and transformations. It is a lightweight pandas-based machine learning framework and can be used seamlessly with existing python machine learning and statistics tools.

4. NumPy: When it comes to scientific computing, NumPy is one of the fundamental packages for Python providing support for large multidimensional arrays and matrices along with a collection of high-level mathematical functions to execute these functions swiftly. NumPy relies on BLAS and LAPACK for efficient linear algebra computations. NumPy can also be used as an efficient multi-dimensional container of generic data.

21 open source python libraries you should know

The various NumPy installation packages can be found here.

5. Pipenv: The officially recommended tool for Python in 2017 – Pipenv is a production-ready tool that aims to bring the best of all packaging worlds to the Python world. The cardinal purpose is to provide users with a working environment which is easy to set up. Pipenv, the “Python Development Workflow for Humans” was created by Kenneth Reitz for managing package discrepancies. The instructions to install Pipenv can be found here.

6. TensorFlow: The most popular deep learning framework, TensorFlow is an open-source software library for high-performance numerical computation. It is an iconic math library and is also used for machine learning and deep learning algorithms. Tensorflow was developed by the researchers at the Google Brain team within Google AI organisation, and today it is being used by researchers for machine learning algorithms, and by physicists for complex mathematical computations. The following operating systems support TensorFlow: macOS 10.12.6 (Sierra) or later; Ubuntu 16.04 or later; Windows 7 or above; Raspbian 9.0 or later.

7. Bob: Developed at Idiap Research Institute in Switzerland, Bob is a free signal processing and machine learning toolbox. The toolbox is written in a mix of Python and C++. From image recognition to image and video processing using machine learning algorithms, a large number of packages are available in Bob to make all of this happen with great efficiency in a short time.

8. PyTorch: Introduced by Facebook in 2017, PyTorch is a Python package which gives the user a blend of 2 high-level features – Tensor computation (like numpy) with strong GPU acceleration and developing Deep Neural Networks on a tape-based auto diff system. PyTorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.

9. PyBrain: PyBrain contains algorithms for neural networks that can be used by entry-level students yet can be used for state-of-the-art research. The goal is to offer simple, flexible yet sophisticated and powerful algorithms for machine learning with many pre-determined environments to test and compare your algorithms. Researchers, students, developers, lecturers, you and me – we can all use PyBrain.

21 Open Source Python Libraries you should know

10. MILK: This machine learning toolkit in Python focuses on supervised classification with a gamut of classifiers available: SVM, k-NN, random forests, decision trees. A range of combination of these classifiers gives different classification systems. For unsupervised learning, one can use k-means clustering and affinity propagation. There is a strong emphasis on speed and low memory usage. Therefore, most of the performance-sensitive code is in C++. Read more about it here.

11. Keras: It is an open-source neural network library written in Python designed to enable fast experimentation with deep neural networks. With deep learning becoming ubiquitous, Keras becomes the ideal choice as it is API designed for humans and not machines according to the creators. With over 200,000 users as of November 2017, Keras has stronger adoption in both the industry and the research community even over TensorFlow or Theano. Before installing Keras, it is advised to install TensorFlow backend engine.

12. Dash: From exploring data to monitoring your experiments, Dash is like the frontend to the analytical Python backend. This productive Python framework is ideal for data visualization apps particularly suited for every Python user. The ease which we experience is a result of extensive and exhaustive effort. 

13. Pandas: It is an open-source, BSD licensed library. Pandas enable the provision of easy data structure and quicker data analysis for Python. For operations like data analysis and modelling, Pandas makes it possible to carry these out without needing to switch to more domain-specific language like R. The best way to install Pandas is by Conda installation 

21 open source python libraries you should know about

14. Scipy: This is yet another open-source software used for scientific computing in Python. Apart from that, Scipy is also used for Data Computation, productivity, and high-performance computing and quality assurance. The various installation packages can be found here. The core Scipy packages are Numpy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.

15. Matplotlib: All the libraries that we have discussed are capable of a gamut of numeric operations but when it comes to dimensional plotting, Matplotlib steals the show. This open-source library in Python is widely used for publication of quality figures in a variety of hard copy formats and interactive environments across platforms. You can design charts, graphs, pie charts, scatterplots, histograms, error charts, etc. with just a few lines of code. 

21 open source python libraries you should know

The various installation packages can be found here.

16. Theano: This open-source library enables you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. For a humongous volume of data, handcrafted C codes become slower. Theano enables swift implementations of code. Theano can recognise unstable expressions and yet compute them with stable algorithms which gives it an upper hand over NumPy. Follow the link to read more about Theano. The closest Python package to Theano is Sympy. So let us talk about it.

17. SymPy: For all the symbolic mathematics, SymPy is the answer. This Python library for symbolic mathematics is an effective aid for computer algebra system (CAS) while keeping the code as simple as possible to be comprehensible and easily extensible. SimPy is written in Python only and can be embedded in other applications and extended with custom functions. You can find the source code on GitHub. 

18. Caffe2: The new boy in town – Caffe2 is a Lightweight, Modular, and Scalable Deep Learning Framework. It aims to provide an easy and straightforward way for you to experiment with deep learning. Thanks to Python and C++ API’s in Caffe2, we can create our prototype now and optimize later. You can get started with Caffe2 now with this step-by-step installation guide.

19. Seaborn: When it comes to visualisation of statistical models like heat maps, Seaborn is among the reliable sources. This Python library is derived from Matplotlib and closely integrated with Pandas data structures. Visit the installation page to see how this package can be installed

20. Hebel: This Python library is a tool for deep learning with neural networks using GPU acceleration with CUDA through pyCUDA. Right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Other models such as Autoencoder, Convolutional neural nets, and Restricted Boltzman machines are planned for the future. Follow the link to explore Hebel.

21. Chainer: A competitor to Hebel, this Python package aims at increasing the flexibility of deep learning models. The three key focus areas of chainer include :

a. Transportation system: The makers of Chainer have consistently shown an inclination towards automatic driving cars and they have been in talks with Toyota Motors about the same.

b. Manufacturing industry: From object recognition to optimization, Chainer has been used effectively for robotics and several machine learning tools.

c. Bio-health care: To deal with the severity of cancer, the makers of Chainer have invested in research of various medical images for early diagnosis of cancer cells.

The installation, projects and other details can be found here.

So here is a list of the common Python Libraries which are worth taking a peek at and if possible familiarizing yourself with. If you feel there is some library which deserves to be in the list do not forget to mention it in the comments.

 

15 Most Common Data Science Interview Questions

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Data Science is a comparatively new concept in the tech world, and it could be overwhelming for professionals to seek career and interview advice while applying for jobs in this domain. Also, there is a need to acquire a vast range of skills before setting out to prepare for data science interview. Interviewers seek practical knowledge on the data science basics and its industry-applications along with a good knowledge of tools and processes. Here is a list of 15 most common data science interview questions that might be asked during a job interview. Read along.

 

1. How is Data Science different from Big Data and Data Analytics?

Ans. Data Science utilizes algorithms and tools to draw meaningful and commercially useful insights from raw data. It involves tasks like data modelling, data cleansing, analysis, pre-processing etc. 

Big Data is the enormous set of structured, semi-structured, and unstructured data in its raw form generated through various channels.

And finally, Data Analytics provides operational insights into complex business scenarios. It also helps in predicting upcoming opportunities and threats for an organization to exploit.

data science vs big data vs data analytics
How is Data Science different from Big Data and Data Analytics?

2. What is the use of Statistics in Data Science?

Ans. Statistics provides tools and methods to identify patterns and structures in data to provide a deeper insight into it. Serves a great role in data acquisition, exploration, analysis, and validation. It plays a really powerful role in Data Science.

 

3. What is the importance of Data Cleansing?

Ans. As the name suggests, data cleansing is a process of removing or updating the information that is incorrect, incomplete, duplicated, irrelevant, or formatted improperly. It is very important to improve the quality of data and hence the accuracy and productivity of the processes and organization as a whole.

 

4. What is a Linear Regression?

The linear regression equation is a one-degree equation of the form Y = mX + C and is used when the response variable is continuous in nature for example height, weight, and the number of hours. It can be a simple linear regression if it involves continuous dependent variable with one independent variable and a multiple linear regression if it has multiple independent variables. 

 

5. What is logistic regression?

Ans. When it comes to logistic regression, the outcome, also called the dependent variable has a limited number of possible values and is categorical in nature. For example, yes/no or true/false etc. The equation for this method is of the form Y = eX + e – X

 

6. Explain Normal Distribution

Ans. Normal Distribution is also called the Gaussian Distribution. It has the following characteristics:

a. The mean, median, and mode of the distribution coincide

b. The distribution has a bell-shaped curve

c. The total area under the curve is 1

d. Exactly half of the values are to the right of the centre, and the other half to the left of the centre

 

7. Mention some drawbacks of the linear model

Ans. Here a few drawbacks of the linear model:

a. The assumption regarding the linearity of the errors

b. It is not usable for binary outcomes or count outcome

c. It can’t solve certain overfitting problems

 

8. Which one would you choose for text analysis, R or Python?

Ans. Python would be a better choice for text analysis as it has the Pandas library to facilitate easy to use data structures and high-performance data analysis tools. However, depending on the complexity of data one could use either which suits best.

 

9. What steps do you follow while making a decision tree?

Ans. The steps involved in making a decision tree are:

a. Pick up the complete data set as input

b. Identify a split that would maximize the separation of the classes

c. Apply this split to input data

d. Re-apply steps ‘a’ and ‘b’ to the data that has been divided 

e. Stop when a stopping criterion is met

f. Clean up the tree by pruning

Decision tree
Steps involved in making a Decision Tree

10. What is Cross-Validation? 

Ans. It is a model validation technique to asses how the outcomes of a statistical analysis will infer to an independent data set. It is majorly used where prediction is the goal and one needs to estimate the performance accuracy of a predictive model in practice.

The goal here is to define a data-set for testing a model in its training phase and limit overfitting and underfitting issues. The validation and the training set is to be drawn from the same distribution yo avoid making things worse.

 

11. Mention the types of biases that occur during sampling?

Ans. The three types of biases that occur during sampling are:

a. Self-Selection Bias

b. Under coverage bias

c. Survivorship Bias

 

12. Explain the Law of Large Numbers

Ans. The ‘Law of Large Numbers’ states that if an experiment is repeated independently a large number of times, the average of the individual results is close to the expected value. It also states that the sample variance and standard deviation also converge towards the expected value.

 

13. What is the importance of A/B testing

Ans. The goal of A/B testing is to pick the best variant among two hypotheses, the use cases of this kind of testing could be a web page or application responsiveness, landing page redesign, banner testing, marketing campaign performance etc. 

The first step is to confirm a conversion goal, and then statistical analysis is used to understand which alternative performs better for the given conversion goal.

 

14. What are over-fitting and under-fitting?

Ans. In the case of over-fitting, a statistical model fails to depict the underlying relationship and describes the random error and or noise instead. It occurs when the model is extremely complex with too many parameters as compared to the number of observations. An overfit model will have poor predictive performance because it overreacts to minor fluctuations in the training data.

In the case of underfitting, the machine learning algorithm or the statistical model fails to capture the underlying trend in the data. It occurs when trying to fit a linear model to non-linear data. It also has poor predictive performance.

 

15. Explain Eigenvectors and Eigenvalues

Ans. Eigenvectors depict the direction in which a linear transformation moves and acts by compressing, flipping, or stretching. They are used to understand linear transformations and are generally calculated for a correlation or covariance matrix. 

The eigenvalue is the strength of the transformation in the direction of the eigenvector. 

 

Stay tuned to this page for more such information on interview questions and career assistance. If you are not confident enough yet and want to prepare more to grab your dream job in the field of Data Science, upskill with Great Learning’s PG program in Data Science Engineering, and learn all about Data Science along with great career support.

Critical skill-sets to make or break a data scientist 

Reading Time: 4 minutes

Ever since data took over the corporate world, data scientists have been in demand. What further increases the attractiveness of this job is the shortage of skilled experts. Companies are willing to pour their revenue into the pockets of data scientists who have the right skills to put an organization’s data at work.

However, that does not mean it is easy for candidates to grab a job at renowned organizations. If you’ve been wanting to establish a career in data science, know that it takes the right set of skills to be considered worthy of the position.

What exactly then do you need to become an in-demand data scientist?

Here are a few valuable skills required for data scientist to inculcate before hitting the marketplace looking for your ideal job.

Programming or Software Development Skills

Data scientists need to toy with several programming languages and software packages. They need to use multiple software to extract, clean, analyze, and visualize data. Therefore, an aspiring data scientist needs to be well-versed with:

– Python – Python was not formally designed for data science. But, now that data analytics and processing libraries have been developed for Python, giants such as Facebook and Bank of America are using the language to further their data science journeys. This high-level programming language is powerful, friendly, open-source, easy to learn, and fast.

– R – R was once used exclusively for academic purposes, but a number of financial institutions, social networking services, and media outlets now use this language for statistical analysis, predictive modelling, and data visualization. This is a reason why R is important for aspiring data scientists to get their hands on.

– SQL – Structured Query Language is a special-purpose language that helps manage data in relational database systems. SQL helps you in inserting, querying, updating, deleting, and modifying data held in database systems. 

– Hadoop – This is an open-source framework that allows distributed processing of large sets of data across computer clusters using simple programming models. Hadoop offers fault tolerance, computing power, flexibility, and scalability in processing data.

Problem Solving and Risk Analysis Skills

Data scientists need to maintain exceptional problem-solving skills. Organizations hire data scientists to work on real challenges and attempt to solve them with data and analytics. This needs an appetite to solve real-world problems and cope with complex situations. 

Additionally, aspiring data scientists also need to be a master at the art of calculating the risks associated with specific business models. Since you will be responsible for designing and installing new business models, you will also be in charge of assessing the risks that entail them. 

skills required for data scientist
Summary of critical skills required for data scientists

Process Improvement Skills

Most of the data science jobs in this era of digital transformation have to deal with improving legacy processes. As organizations move closer to transformation, they need data scientists to help them replace traditional with modern.

As a data scientist, it falls upon you to find out the best solution to a business problem and improve relevant processes or optimize them. 

It makes a lot of sense for data scientists to develop a personalized approach to improving processes. If you can show your potential employer that you can enhance their current business processes, you will significantly increase your chances of landing the job.

Mathematical Skills

Unlike many high-paying jobs in computer science, data science jobs need both practical and theoretical understanding of complex mathematical subjects. Here are a few skills you need to master under this set:

– Statistics – No points for guessing this one, but statistics is and will be one of the top data science skills for you to master. This branch of mathematics deals with the collection, analysis, organization, and interpretation of data. Among the vast range of topics you might have to deal with, you’ll need a strong grasp over probability distributions, statistical features, over and undersampling, Bayesian statistics, and dimensionality reduction. 

– Multivariable calculus and linear algebra – Without these technologies, it is hard to curate the modern-day business solutions. Linear algebra happens to be the language of computer algorithms, while multivariable calculus is the same for optimization problems. As a data scientist, you will be tasked with optimizing large-scale data and defining solutions for them in terms of programming languages. Therefore, it is essential for you to have a stronghold over these concepts.

Deep Learning, Machine Learning, Artificial Intelligence Skills

Did you know, as per PayScale, the data scientists equipped with the knowledge of AI/ML get paid up to INR 20,00,000 with an average of INR 7,00,000? Modern-day businesses need their data scientists to have a basic understanding, if not expertise, over these technologies. Since these areas of technology have to do a lot with data, it makes sense for you to have a foundational understanding of these concepts.

Learning the ins and outs of these concepts will highly increase your data science skills and help you stand out from other prospective employees.

Collaborative Skills

It is highly unlikely for a data scientist to work in solitude. Most companies today house a team of data science experts who work on specific classes of problems together. Even if not in a team of data scientists, you will definitely need to collaborate with business leaders and executives, software developers, and sales strategists among others.

Therefore, when putting all of the necessary skills in perspective, do not forget to inculcate teamwork and collaborative skills. Define the right ways of bringing issues in front of people and explaining your POV without exerting dominance.

It might also help you to be able to explain data science concepts and terminologies in a simple language to non-experts.

For the year 2019, the total number of analytics and data science job positions available are 97,000, which is more than 45% as compared to the last year. Trends like this act as a magnet to attract fresh graduates towards a career in Data Science. As a data scientist, you need to wear multiple hats and ace them all. Since the field is currently expanding and evolving, it is hard to predict everything that a data scientist needs to know. However, start by working on these preliminary skills required for data scientist and then move your way up.

If you are interested in moving ahead with a career in Data Science, then you should start inculcating the above-mentioned skills to improve your employability. Upskilling with Great Learning’s PG program in Data Science Engineering will do the most of it for you!