Data Science Skills Study 2019

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Data Science Top Trends

As is true with every emerging field of technology, data science has been undergoing several transformations every year. Data science professionals need to be informed about the constant changes and new advances in the field to stay on top of their game. As the learning of the domain reaches new depths, knowing the new tools and technologies become crucial to avoid disruptions at the workplace.

However, keeping track of all the new changes and advances can be quite challenging at times. At Great Learning, we understand these challenges, and with that in mind have curated reports on the trends of the domain in 2019. The following inferences highlight the essential new developments in data science languages, methods, tools and more to help you decide your learning needs.

Preferred Data Science Language

Python has emerged as the most used programming language among data scientists. Almost 68% of data scientists cite Python as their preferred data science language, replacing R which was popular even a couple of years back. SQL and SAS have also witnessed a steep decline in use despite their versatility. 

Popular Data Science Methods at Work

Logistic Regression dominates the domain when it comes to popular data science methods used at work. It is closely followed by Decision Trees and Neural Network which have 58% and 44% user base respectively.

Popular Python General Purpose Library

Since Python is the largest programming language in the world, data scientists frequently refer to Python libraries to analyse large amounts of data. Pandas top the list of Python libraries with 42% popularity, followed by Numphy at 30%, Sklearn at 13% and MatPlotLib at 7%.

Preferred Data Science Tools

Open source tools remain the most used by data scientists and have grown in popularity compared to last year. Only 5% of data scientists state that they prefer using custom-made tools that are tailored for very specific uses.

Preferred Data Science Visualisation Tools

Dashboards and visualisation tools are crucial for formatting and presentation. As it impacts decision making directly, data scientists are very particular about the tools they use to represent their findings. Tableau has emerged as the most preferred visualisation/dashboard tool with 56% of the votes. Other popular choices include Microsoft Power BI, IBM Watson Analytics, and SAP Analytics Cloud.

Poplar Cloud Providers

Amazon Web Services take the cake with a whopping 43% of the votes. Google cloud follows close with 33% and Microsoft Azure is fast becoming another popular choice with 16% votes.

The trend in Learning Resources

Thanks to the   nature of the domain, constant learning is a mandatory part of a data scientist’s journey. 78% of data scientists resort to Youtube video tutorials for learning new techniques while the rest follows the old school way of reading books. Some data scientists also refer to MOOCs to upskill themselves.

Finding Open Data

Sourcing clean open data can be quite a task sometimes. Thankfully GitHub, government websites and university websites are ready sources from where you can find clean open data. Manual data creation also remains a popular choice for clean open data.

Most Used OS for Data Science

Data science tools often face compatibility challenges with operating systems. Windows OS happens to be compatible with most data science tools while Linux is the most secure platform to use. A minuscule percentage of data scientists use MacOS.

Favourite Development Environment

IDE or integrated development environment is very important for hosting all kinds of data science processes. Notebook, R Studio and PyCharm are the top choices for IDE in the domain.

Popular Neural Network Architectures

Convolutional neural network is the most frequently used neural network of 2019 apart from Feedforward neural network for network architectures.

If 2018 was all about the exponential growth of data science, 2019 and the coming years will be more about how professionals are transitioning into the domain. The demand for data science expertise is creating different kinds of opportunities for both specialists and generalist skills. Recruiters are using innovative ways to test and hire candidates. Companies are even creating internal training programs to upskill their employees. After all, a successful career in data science needs continuous learning. You can check out data science programs here and here to understand course structure and curriculum. 

How to Secure Your Company Website from Hackers

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It’s surprising how business owners invest heavily into all aspects of their business except for their website security. It’s especially surprising, given how cyber crime is increasing at an alarming rate today. Most businesses don’t do enough about securing their websites because they labour under the misconception that their websites do not host anything that could be of any value to the attackers or that cyber crimes are limited to just theft. Truth is, if you leave your website unsecured, it will become exposed to a number of cyber crimes ranging from theft, manhandling, destruction, deletion and much more. It is almost like leaving the doors of your home open for strangers. Hence, it becomes extremely important to protect websites from becoming vulnerable and falling prey to malicious hackers. Follow the steps listed below to ensure that your website is well protected.

Keep Your Website Platform and Software Updated

Keeping all the website software updated is the first step towards securing your website. Un-updated software is one of the biggest reasons for websites getting hacked. The moment hackers find a security hole in the software, they will be quick to abuse it. Joomla, WordPress, Umbraco and other CMS providers keep releasing new patches and updates to plug any security holes in their software. Update your software whenever a new version is released. For managed hosting solutions, the hassles are less as the hosting companies ensure that their systems are up-to-date. It is also essential to clean your website of old and unused plugins since those are the weak spots which hackers target.

You can also use tools like RubyGems, Composer or npm to manage software dependencies. Often, developers tend to overlook the security vulnerabilities of a package while working on them. An easy way to solve this issue is by installing tools like Gemnasium which will notify you every time your software faces vulnerability and requires your attention. 

Keep Out SQL Injection

SQL injection attackers use URL parameters or a web form field to gain access and control over website database. It is easy for hackers to insert rogue codes in your query if you are using standard Transact SQL. Once the attackers have control over your database they can manipulate it to extract information, or even delete the data. The best way to prevent these attacks is by using queries that have multiple parameters. Parameterised queries are part of almost all web languages where you can choose and implement values of your own. 

Use HTTPS

While working on the website, it is important to ensure that the content is well protected even when it is in transit. Web hackers often intercept and manipulate data in transit before it reaches the server. Attacks can start with simple breaches – when attackers posing as website users steal cookie authentication requests and use that to take over login sessions. HTTPS is a proven method to avert these kinds of attacks. HTTPS ensures encryption of private or sensitive data so that it doesn’t land in the wrong hands. You can use automated frameworks and platforms to set up HTTPS easily without spending a fortune on it. SSL certificate, for example, is used to ensure safe transfer of data between websites and servers. Google has recently started notifying websites if they don’t use HTTPS and takes it a step further by boosting your SEO ranking if it does. These certificates are inexpensive but secure ways of protecting your website information.

 

Install a Web Application Firewall

Installing a web application firewall is like putting a protective shield over your website. WAF or web application firewall can be both software or hardware based. There are several cloud based security providers who are making safety applications available in the market today. These applications contain enterprise level security measures but at much reduced prices. These solutions monitor the quality of incoming traffic to your website to ensure that no malpractioners are targeting your website. WAF is that defence line which protects your website against a range of attacks including SQL injections, cross site scripting, SPAM, brute force attacks and more. With cloud based plug-and-play web application firewall, you won’t even need security experts to look over the process – the applications are quite self-functioning. 

Hide Your Admin Directories

Hackers often target website sources and admin directories to hack into a system. Admin directories contain all kinds of crucial information- from the data that ensures a smooth running of your website to the permissions and conditions that rule how users interact with your website. Needless to say, if hackers gain access to this file, they can cause serious damage to your business. Hackers can use really simple tricks like running a script through your web directories to scan files with ‘admin’ or ‘login’ written on it. Locating these files make it easier for them to hack into it. As a counter trick, what you can do is – rename these files cleverly so that hackers won’t identify them as the admin directory. Pick inconspicuous names that dont give themselves away. As an extra precautionary step, make sure only your webmasters know the location and details of this file.

Prevent Cross-Site Scripting

Cross site scripting attacks your website by injecting malicious javascript into your site and infecting visitors who are exposed to that code. Similar to SQL injection, cross site scripting can be prevented by using parameterised queries. Use these parameters to define the inputs clearly so that no foreign codes can slip in. Front-end frameworks like Angular and Ember provide XSS protection. Tools like content security policy can also protect your site from cross site scripting. 

Secure File Uploads

If you are allowing your website users to upload files (whether to change the avatar or more), you are essentially making your website susceptible to hacking. Even if you use security systems to check through your website regularly, file uploads can cause serious damage by giving hackers complete access to your site data. Of course, the best way to deal with this is by blocking access to uploaded files but alternately, you can also store these files outside your root directory. This way you can access them through scripts and limit access for users. 

Always check the file extensions but don’t just count on that as there are ways for the threat files to get through.

Conclusion

Securing your website is not just a moral obligation but a legal requirement sometimes, especially if it has sensitive user data. Attacks can happen anytime and if it happens it will be fast, leaving you no room for preparation – so prepare in advance. Adding even small, inexpensive security measures can go a long way in preventing attacks. Website owners must take cyber security as seriously as they take sales or customer relationship management (if not more). Include these aforementioned steps in your security process to ensure that your website is not an easy target for hackers.

Your Weekly Essential Guide to Artificial Intelligence – September Part I

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Artificial Intelligence, a term which was coined by a Dartmouth professor over sixty years ago, has today become a prevalent tool of modern business. From the communications industry to the health industry, AI is redefining the ways in which humans and machines interact (thankfully, mostly for the better). Data engineers around the world are making machines replicate the intricacies of human reasoning faster than you can imagine. The following articles shed light on the various ways in which artificial intelligence is driving major changes in education, health, technology and more. Keep reading.

Elon Musk: Humanity Is a Kind of ‘Biological Boot Loader’ for AI

In a recent Artificial Intelligence conference, Alibaba co-founder Jack Ma and Tesla CEO Elon Musk, took the stage to debate on the implications of AI for humanity. Quite naturally, the debate brought up some interesting aspects of AI – how Neuralink is decoding the human brain to aid patients with neurological disorder, how it can facilitate man’s journey to Mars, or even how 80 percent of statistics around AI is false. While Musk talked about how AI is outpacing our ability to understand it, Ma thinks that it will create new opportunities for humankind. 

Esteemed consortium launch AI natural language processing benchmark

Natural language processing now has a new benchmark for analysing its applications, thanks to an esteemed consortium, featuring experts from Google DeepMind, Facebook AI, New York University, and the University of Washington. The resultant platform, SuperGlue replaces its predecessor Glue to bring varied new formats, nuanced questionnaires and other challenging activities to test NLP applications. According to Facebook AI, SuperGLUE is “much harder benchmark with comprehensive human baselines,” compared to Glue.

A Molecule Designed By AI Exhibits ‘Druglike’ Qualities

Insilico Medicine, a startup that generates potential drugs using artificial intelligence, has recently identified a new molecule that can bind with a protein associated with tissue scarring. If validated, this could become a major discovery but the expenses and the timeline keeps getting higher. Thankfully, with AI there’s a chance of putting the idea to test and generate results sooner. A team of AI experts and collaborators from the University of Toronto generated 30,000 designs in 21 days which means the premises of cost cutting and fast tracking this development looks promising. 

‘Sense of urgency’, as top tech players seek AI ethical rules

The data world witnessed its first Swiss Global Digital Summit this year. Two dozen high-ranking representatives of the global and Swiss economies, as well as scientists and academics, met in Geneva to draw up global ethical standards that should govern AI applications. Microsoft president Brad Smith, voiced his concern over the growing need for such standards saying, “technology be guided by values, and that those values be translated into principles and that those principles be pursued by concrete steps.” ‘Transparency’ and ‘accountability’ were two words frequently used during the course of this summit to highlight the need of the hour regarding AI practises. 

Robots Turn Teachers in Bengaluru School, Thanks to AI 

A man-machine team at one of the private international schools in Bangalore now has robots to complement real teachers during classes. These AI-enabled robots teach almost all the major science subjects and emulate human gestures while imparting lessons in the class. Students have responded positively to this joint human-machine venture so far. The school administration feels that this move will promote learning even more by making it a personalised experience for students. This one-of-a-kind projects is soon to be scaled up to include other subjects and more classes in the future.

For more roundups on AI, watch this space!

 

Your Weekly Essential Guide to Artificial Intelligence – August Part III

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As acknowledged by the major multinational companies, AI will be significant in shaping our socio-economic future. From designing smart weapons to predicting health risks, AI will be changing the way in which the world does business. Engineers have already started preparing for the upcoming surge in AI requirements. With major investments from different industries, the scope for research has also significantly increased. The following articles track the latest developments in the AI field and showcase how it’s impacting business globally. Keep reading.

Nvidia CEO calls AI ‘the single most powerful force’ as Earnings Beat Expectations

Nvidia’s artificial intelligence wing is growing rapidly and bringing the company massive revenues and product demands are often exceeding expectations. Even though gaming remains the primary focus for Nvidia, an increased demand for graphics and AI chips has compelled the company to focus on developing AI applications as well. Nvidia is emerging as one of the major companies in AI, especially in driverless cars and has been investing heavily in building AI chips. The Xavier chip is the result of 4 years of tireless dedication of over 2000 engineers and is one of the best chips available in the market today. The company will continue to drive innovation and establish itself as a major player in the AI sector.

New consortium aims to make Bengaluru hub for industrial AI

A consortium was recently formed by Derick Jose, co-founder of Flutura and a few others with the objective of making Bengaluru one of the top international hubs for artificial intelligence startups. Following China, India has started to scale up its tech entrepreneur presence in the international market by leveraging its unique position in the startup scene. The consortium has already managed to gather early stage fundings so that promising ideas can be put into action at the earliest. “It’s wonderful to see they’re not just building something cool but doing much more to build the ecosystem in India,” said one of the consortium members. Owing to its rich pool of deep tech and engineering talent, Bengaluru is expected to power autonomous operations in industries.

BMW’s Increasing Investment in AI 

Identifying the enormous possibilities of artificial intelligence, multinational companies from across various industries have started investing in AI research to find new AI capabilities – BMW is one of them. BMW funds companies that are directly or indirectly pushing advancements in the automotive ecosystem. This also includes investing in startups which specialise in designing AI functionalities for automatic cars and other related technologies for an enhanced transportation system. For any machine learning system to learn and produce accurate outputs, it must be provided with sufficient amounts of clean data. AI specialists spend a lot of their time collecting, cleaning and labeling data to ensure it is fit to be fed to the systems. 

Artificial intelligence can contribute to a safer world

Security measures for protecting public venues will undergo upgradation with AI. Artificial intelligence will be used to eradicate all possible system errors, identify potential threats and plan counteractions. With the new systems, security applications will be able to detect guns, knives, bombs, and much more security hazards. Furthermore, AI will be able to even distinguish between threatening and non-threatening objects. These smart security systems will check through crowds and queues meticulously without slowing down the traffic flow. 

Center for Data Innovation: U.S. leads AI race, with China closing fast and EU lagging

The USA is leading the front for AI development with China following closely and EU falling behind. China’s determination to lead the market by 2030 is another factor that is driving US policy makers to invest in AI. Since the winners of an AI arms race will rule the global economy, there’s a lot of pressure on US to up its AI game. Even though all three players have equal potential in terms of resources and talent, EU seems to be not focusing on AI as much as the US and China at the moment. 

For more roundups on AI, watch this space!

Your Weekly Essential Guide to Data Science and Analytics – August Part IV

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There’s no denying now that automation will rule our future and data science will direct the path towards automation. An increasing number of data scientists will be employed to help companies extract meaning out of pools of data and new discoveries will be made based on such inferences. That future, surprisingly, isn’t that far from now – the latest developments in data science are testimonials to that. Even as we speak, thousands of data scientists are engaging in new data discoveries and leading the world towards a more data driven future. The following articles capture those trends and influences aptly. Keep reading.

Hotwiring Data Science: AIOps and the Evolution of DevOps for Big Data

Enterprises can help data science teams to build and operate data analytics platform by intercepting and implementing data patterns. Product development teams often face challenges while integrating new models of data analytics into existing applications. Similarly, due to the lack of proper analytics platform, data scientists also struggle to get the most out of ill-formed infrastructures. To help enterprises bridge the gap between data science expectations and real developments, development teams are using AIOps or AI for IT operations. AIOps practices focus on optimising model consumption and data scientist productivity by identifying DevOps patterns that answer AIOps challenges.

DoE Pours $27.6 million into Data Science Research and Development

“The rapid evolution of artificial intelligence, machine learning, and other data science techniques is creating new opportunities for advances in chemistry and material sciences,” said U.S. Secretary of Energy Rick Perry. With this in mind, the US Department of Energy (DOE) has announced $27.6 million funding for targeted research in data science to encourage and aid discovery in chemistry and material sciences. The funding will be provided over the next three years to help a smooth execution of the data science research. This research will propel discoveries in predictive understanding of materials and development of  new alloys, superconductors, catalysts and more. This financial grant towards data science research is expected to have a significant impact on energy production and delivery.

Five Factors Shaping Data Science

The key challenges in the path of data science evolution are quite a few, but that’s not to say that the solutions are not effective. A survey result of 2019 pointed out that 80% of companies have faced stagnation of data science project. These stalled projects have resulted in the waste of resources that could have used elsewhere. Whether it is the shortage of data science talents, lack of actionable data, or complex operational processes, organisations are meeting the challenges of data science projects with innovative solutions to compete in the new AI-driven economy.

Meet Berkeley’s New Data Science Leader

Former Microsoft technical fellow and managing director, Jennifer Tour Chayes will be becoming UC Berkeley’s inaugural associate provost for the department of data science and information and Dean of the School of information. Chayes, a leader in the field of network science, has devoted much of her time studying the ethics of data science and believes that the ethical issues which challenge the world of data science can be addressed by designing algorithms that mitigate biases in data set. 

Welsh University to Study Big Data Science and Offshore Wind

The Welsh Bangor University has secured a GBP of 4.6 million EU fund to fuel the research for low carbon energy systems which includes offshore wind. Data science inferences and studies will be used for deriving the results for energy efficient options. Joint research between Welsh and international organisations and businesses will be further conducted for the new Smart Efficient Energy Centre. The research will focus on new cyberinfrastructure and digital systems to increase the volume and speed of data analytics. The SEEC will become an international hub of excellence in data science research and contribute significantly towards the growth of low carbon energy sectors.

For more digests on data science, watch this space!

Step-by-Step Guide to Becoming a Data Scientist

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The world is moving towards complete digitisation and is expected to generate copious amounts of data in the future. However, to make sense of this data we need specialists who can read, model and organise data in coherent detail. Data science has emerged as an effective means of handling data to extract meaning out of random numbers and figures.

Clearly, Harvard Business Review wasn’t bluffing when they suggested data scientists to have the “sexiest job of the 21st century” – not only is it one of the most crucial profiles in the market today but is also among the highest paid ones. If you are wondering what it takes to become one, we have laid down a step by step guide towards becoming a data scientist here.

Step 1:

A career in data science requires constant learning and upskilling and it cannot be an impulsive decision. If you are planning a long sprint in this direction, make sure you have a suitable background and aptitude. Start by asking yourself the following questions to find out if this path is for you.

– Do you have an educational background in computer science, information technology, mathematics, statistics or a similar branch of study?

– Do programming languages excite you?

– Are you a proactive learner who is willing to pick up the tricks of the trade ahead of the market?

– Do you enjoy handling complex data sets to understand patterns?

Data science might be a rewarding career choice but it requires concerted efforts. A course in data science can help you master the essentials and make you industry-ready. 

Step 2:

If you are from a non-technical background and still want to pursue a career in data science, fret not. You can up your chances of becoming a data scientist by developing skills in the field of applied mathematics and statistics. Market research shows that a considerable number of data scientists hail from a business or economics background. If you are an aspiring candidate with a similar educational background, brush up your skills in mathematics and statistics as a preparatory step.

Step 3:

Master the basics of machine learning as it is one of the most crucial components of data science. It is used for a number of data science applications, ranging from reporting forecasts to identifying data modelling patterns. Familiarity with machine learning tools and techniques will help you to master other data science tools with ease. Once you pick up the basic machine learning tools and functionalities, designing and using algorithms for data modelling will become easier. 

Step 4:

Programming is one of the main requirements in a data science profile. Learn to code so that you can read and analyse data sets. Pick up programming languages like Python, R, SAS and more. Python remains one of the most widely used programming languages owing to its flexibility. Among the querying languages, SQL is prominent, so learning both these programming languages will help you launch your data science careers successfully.

Step 5:

The next step in the course of action should be learning data munging. It is a process of looking through messy data sets to identify and discard redundant data. This cleanup process is a preparatory step towards data analysis. Data munging helps data scientists to analyse and present data in a readable format.

Step 6:

For a data scientist, if data analysis is half of the job, the other half is reporting. Business decision-makers refer to data reports to drive business and generate revenue. But for the data to make sense, it must be put into data visualisation tools like charts, Tableau, d3.js, Raw and more. Data scientists must familiarise themselves with the principles of data communication systems and visual encoding to present data in an easy and readable format.

Step 7:

The best way to fine-tune your skills in data science is by applying that knowledge to practice. Once you have mastered all the theoretical knowledge, start working on projects that replicate real-world data complexities faced by companies. Alternatively, you can also intern at leading data science companies or join bootcamps to get hands-on experience on real data science applications. 

Step 8:

Stay updated on the recent developments in the field of data science. The amount of data generated by the world is increasing each day and in keeping with this exponential growth, data science is also evolving. Data scientists must learn ways of enhancing data tracking and analysing applications to ensure resource optimisation. Constant learning is crucial for data scientists to stay on top of their game. Look for educational and professional development opportunities that will advance your career in data science. 

Step 9: 

Once you have completed your education in data science and gathered experience working on projects and as interns, it’s time to create a portfolio showcasing the same. Update your resume, highlighting your data science skills adequately and start applying for relevant openings. You can prepare for interviews by referring to the most popular data science questions and answers

After you have followed these nine aforementioned steps, your data science career will be all set to take off. With an arsenal full of data science skills, landing a relevant role won’t be difficult, especially if you have worked on projects and have industry-relevant experience. However, in order to keep growing in the field, you must constantly seek challenges and keep learning. Start viewing all kinds of business circumstances as scopes for studying data – start thinking like a data scientist. Courses and certifications will help you stay updated about the latest technologies in the field and give you an edge over your competitions. Great Learning, one of India’s premier education institutes offers courses that cover all the essentials of data science and make professionals industry-ready. Check out a data science program to get a better understanding of the curriculum.

Also, check out the online course, PG program in Data Science and Business Analytics, to learn seamlessly with the comfort of your own place and time.  

 

Different aspects of Data Science and related fields such as Machine Learning

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It is no surprise that data science is THE future of technology and is creating millions of jobs world wide. Tech giants like Facebook, Google, IBM are spending millions of dollars in research and development of different aspects of Data Science like Machine Learning and Artificial Intelligence. It is also one of the most sought after job on job searching websites like Linkedin, Glassdoor and Monster. And if you are wondering what skills a data scientist requires, then read on.

To begin with, let’s talk about what is Data Science?

As the name suggests, Data science deals with ‘data’- large amounts of data. This data is grouped, classified and structured and then useful insights are drawn from it that help the development of businesses. Reading this data though in theory may sound simple, it’s actually not. That’s where the ‘science’ comes into the picture. In order to read the data, many tools and algorithms have to be used to visualise, structure and then read and derive insights.

Data science is used as a rather broader generic term these days, when people use the word Data science they don’t mean the textbook definition of Data Science but rather all the different fields that come under Data Science, like, Data Analytics, Business Analytics, Machine Learning and Artificial Intelligence.

Each field is unique in it’s own way and perform their own tasks and functions.

Data science flow-chart

dse

This chart shows the flow in Data science, right from obtaining the data to predicting the insights, along with all the skills and tools required for that particular stage of the flow-chart.

  1. Data collection
  2. Data wrangling
  3. Data exploration
  4. Data modelling
  5. Report

Step 1:

Obtaining the Data

This is obviously one of the first and foremost steps. First you need to identify what kind of data you want to analyse, and then you need to export this to an exel or csv file. The next step would be to make this data easily readable. Basically, it should be labelled and structured the right way so that it is easy to analyse.

Skills and tools required

  • Database management : SQL
  • Understanding the database and what it represents
  • Retrieving raw unstructured data in the form of text, docs, photos, videos etc.
  • Distributed storage : hadoop, spark, or apache

Step 2:

Scrubbing or cleaning the data

This is an important step because before you are able to read the data, you must make sure it is in a perfectly readable state, without any mistakes, no missing values or wrong values, and the data has to be consistent throughout, because the data is the most important part in this field.

Skills and tools required

  • Scripting language – Python, R, SAS
  • Data wrangling tools – Python Pandas, R
  • Distributed processing – Hadoop, Mapreduce/spark

Step 3:

Exploratory Data Analytics

Now that your data is clean and readable, it’s time to get to the real work. Analysing the data. This is done by visualising the data in various ways and identifying patterns and spotting anything out of the ordinary. In order to analyse the data you must have an eye or attention to detail and must be able to think out of the box to identify anything out of place. And then based on this analysis, come with solutions. In short this is what a Data Analyst does.

Skills and tools required

  • Python libraries – Numpy, Matplotlib, Pandas, Scipy
  • R libraries  – GGplot2, Dplyr
  • Inferential statistics
  • Data visualisation
  • Experimental design

Step 4:

Modelling or Machine Learning

Machine Learning is an application of Artificial Intelligence, in which, a machine can follow commands and rules (algorithms) and come with predictive solutions without any human supervision.

The engineer or scientist writes down a set of instructions for the Machine Learning algorithm to follow based on the data that has to be analysed and come up with the right output after learning through the data and instructions.

After cleaning up the data and finding out essential features through the data exploration phase, using a statistical model as a predictive tool will enhance your overall decision making.

Skills and tools required

  • Machine learning – supervised, unsupervised and reinforcement machine learning
  • Evaluation methods
  • Machine learning libraries – Python (sci-kit learn) / R (CARET)
  • Linear algebra and multivariate calculus

Step 5:

Interpreting or ‘data storytelling’

This is the final step, in which you uncover your finding to your boss or company, the most important step in this would be your ability to explain your results.

You must be able to explain this to anyone with a non technical background. Hence the term ‘storytelling’.

In order to understand how the data can affect the business or how your solution helps to provide better business solutions, you must also have a understanding of the business domain.

Skills and tools required

  • Knowledge of your business domain
  • Data visualisation tools – tableau, GGplot, Seaborn etc.
  • Communication – presentation skills, both verbal and written

This marks the end of the Data Science flow-chart. Now that you know what skills and tools you need to know in order to become a data scientist, you can now start to learn all these tools and enter into the vast field yourself.

You can start your learning journey with Great Learning, a premier learning institute which designs courses especially tailored for people with no history or knowledge in this field of data science.

Click here to start your journey!

 

Difference between Business Analyst & Data Scientist

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If there’s one thing that has emerged as a force to be reckoned with in the world today – it’s data. Data is driving and shaping modern businesses exponentially. However, data in itself doesn’t hold much value for businesses unless it is analysed and categorised. Experts who dabble in data analytics can either be from a data science or business analytics background. While both data scientists and business analysts are often seen working in close collaboration in a data driven environment, each of the roles involves different tasks and responsibilities. 

Both data science and business analytics are popular career choices for young professionals today. If the myriad ways in which data work fascinates you then you can choose from either of the two career paths after considering your educational background, experience, skills and interests. To help you choose a career path, we have listed down the essentials and requirements of each of these roles.

A Comparative Analysis between Data Science and Business Analytics

Data scientists are professionals responsible for analysing, preparing, formatting, and maintaining information. It involves using skills pertaining to computer science, mathematics and statistics. Data scientists are also responsible for developing algorithms and drawing data inferences. Since data science aims at unveiling complex data patterns by studying and understanding data sets, it is important that data scientists are well versed in multidisciplinary skill sets.

Business analysts, on the other hand, are professionals who look into the ever changing needs of any business and assist them in implementing those changes. Business analysis combines integrative skills like analytics, business acumen and domain knowledge. Business analysts are responsible for a range of tasks including understanding business requirements, laying out plans and  developing actionable insights. They form a bridge of communication between various departments in a business organisation to execute any business plan.

Both these roles are in fact, similar in a lot of ways, since both involve data gathering, inference accumulation and data modelling. The scope of data science and business analytics often overlap and the skill sets are not mutually exclusive. In any business environment, data scientists and business analysts work closely to understand and implement strategies. However, there are certain differences between these two branches that aspiring professionals must consider to understand which is best suited for them. Typically, data science can be taken up by early career professionals but business analytics is better suited for professionals with experience in business development, technology and project management.

Business Analyst Job Description

Let’s look into a sample business analyst job description to understand the various tasks and responsibilities involved in the role-

Overview:

We are looking for a business analyst for finance who will be responsible for leading projects, improving processes and supporting systems used by the Finance and Accounting departments as business requirements evolve. The ideal candidate will work closely with business stakeholders from teams within Finance and Accounting in all geographies to understand business challenges. Additionally he/she will document all business processes and requirements to meet those challenges. He/she will drive process improvement ideas with a focus on – scoping, coordinating, planning, executing testing, and executing launch activities, and provide ongoing support. He/she will also be responsible for triage and analysis of production support issues.

Job Responsibilities:

– Engage with our existing and prospective customers and help them to adopt products and solutions to meet their business requirements

– Ensure consistent growth in product awareness, adoption and usage by customers

– Showcase product and solution concepts via presentations, demos, user evangelization and effective documentation

– Lead discovery sessions with IT and business users to understand the client’s business objectives and system/application needs

– With an excellent understanding of product features and related technologies, design the solution that best meets the client’s requirements

– Proactively create documentary artifacts like business cases, usage scenarios, solution blueprints, FAQs, meeting notes… etc.

– Lead or work with other customer success teams to ensure successful completion of project milestones for production and the initial rollout phase of the project

– Communicate progress and expectations, escalate problems for awareness and resolution

– Lead client training sessions

– Support clients and play a key role in promoting solution adoption and usage

– Provide regular and adequate end user feedback to the product team

Job Requirements:

– A technical degree (Engineering, MCA) or business degree (MBA, BBA) from a reputed institute with a minimum of 4-5 years of experience in software or consulting industry

– Must be able to manage multiple projects utilizing strong planning and organisational skills

– Outstanding verbal, written and presentation skills to demonstrate solution concepts

– Strong interpersonal skills with ability to influence and build effective customer relationships

– Experience with general consulting skills that include team facilitation, business case development, strong business analysis skills, process mapping, and business process redesigning.

– Systems implementation skills: requirements/process analysis, conceptual and detailed design, configuration, testing, training, change management, and support

– Ability to set and manage customer expectations, and work independently on project assignments.

– Must be able to travel, providing on-site consulting work to clients when required and have the ability to work remotely from the office.

Good to Have:

– Enterprise-level business project experience with strong process analysis, design, delivery and documentation skills

– Experience working for leading technology consulting companies

– Knowledge of information security procedures and practices

– A certification in the knowledge areas related to information security, business – process management or IT infrastructure

Data Scientist Job Description:

The following sample job description will help you understand the responsibilities handled by data scientists.

Job Overview:

We are looking for a highly skilled, experienced and passionate data-scientist who can come on-board and help create the next generation of data-powered tech product. The ideal candidate would be someone who has worked in a Data Science role before wherein he/she is comfortable working with unknowns, evaluating the data and the feasibility of applying scientific techniques to business problems and products, and have a track record of developing and deploying data-science models into live applications. Someone with a strong maths, stats, data-science background, comfortable handling data (structured+unstructured) as well as strong engineering know-how to implement/support such data products in Production environment.

Job Responsibilities:

– Demonstrate and drive deep technical expertise in solving real world retail business problems through the application of machine learning

– Collaborate with other team members both within and outside the data science team to create and deliver world class data science products

– Act as an SME on the floor and help build data science capabilities

– Preparing monthly sprint plans, prioritising requests from partner product teams

– Partnering with the product team to create key performance indicators and new methodologies for measurement

– Translating data into actionable insights for the stakeholders

– Automate reporting for weekly business metrics, identify areas of opportunity to automate and scale ad-hoc analyses

Job Requirements:

– 3+ years of experience in analytics, data science, machine learning or comparable role Bachelor’s degree in Computer Science, Data Science/Data Analytics, Maths/Statistics or related discipline

– Experience in building and deploying Machine Learning models in Production systems

– Strong analytical skills: ability to make sense out of a variety of data and its relation/applicability to the business problem or opportunity at hand

– Strong programming skills: comfortable with Python – pandas, numpy, scipy, matplotlib; Databases – SQL and noSQL

– Strong communication skills: ability to both formulate/understand the business problem at hand as well as ability to discuss with non data-science background stakeholders

– Comfortable dealing with ambiguity and competing objectives

Good to Have:

– Experience in Text Analytics, Natural Language Processing

– Advanced degree in Data Science/Data Analytics or Maths/Statistics

– Comfortable with data-visualisation tools and techniques

– Knowledge of AWS and Data Warehousing

– Passion for building data-products for Production systems – a strong desire to impact the product through data-science techniques

Data scientists and business analysts are expected to constantly upskill and keep abreast of the latest technologies and developments in their respective fields. Clearly, the decision cannot be an impulsive one. Refer to the curriculum of data science and business analysis for further details so that you are certain of the path you choose.

Your Essential Weekly Guide to Data Science and Analytics – August Part III

Reading Time: 2 minutes

Data science has steadily grown in popularity owing to the rise of digitization. As businesses all over the world embrace data, the market for data science and analytics grow manifold. Modern infrastructure uses data to drive business and generate revenue, so companies are hiring data experts in acknowledgement of this growing trend. The following articles show how data science helps organizations to make data driven decisions. 

Colleges are Using Big Data to Track Students in an Effort to Boost Graduation Rates

Colleges and universities are resorting to high-tech data solutions like predictive analysis to find trends in dropout rates among students. This data-driven path to graduation is helping students overcome barriers and address their issues while attending college. This is an exciting instance where data and analytics have actually improved the graduation rates and pushed students towards chasing a successful career.

ASU Library Opens Center for Data Science, Research Collaboration

As the world is gravitating towards data, it is becoming crucial for business leaders to get a hands-on knowledge of data. Arizona State University understands this requirement and intends to build the library as a critical resource to support all data enthusiasts. “Data science isn’t done in isolation. It’s inherently collective and interdisciplinary, which is why ASU is the perfect place for it,” said Simeone, an assistant research professor affiliated with the Biosocial Complexity Initiative, the Department of English, the Institute for Social Science Research, and the School of Sustainability.

Uber’s Data Science Vision: Empowering All To Become Data Scientists

World’s largest transportation network company wants all its employees to function in their respective roles like a data scientist. The driving idea behind this decision is the fact that data insights will help the company in fostering better customer relations. The company intends to accurately forecast demand and supply metrics to improve user experience. Employees are encouraged to refer to data analytics model to understand consumer behaviour towards that end.

Nike acquires Celect, Adds to Data Science Team

Nike is acquiring Celect, a retail analytics company to build its internal team of data science experts. This team will work towards improving the Consumer Direct Offense Plan to deliver an enhanced user experience. The acquisition of Celect follows Nike’s acquisition of Zodiac, another data analytics company last year, indicating that the sports brand is investing heavily in data to make their business data-driven.

Zindi rallies Africa’s data scientists to crowd-solve local problems

Cape Town based startup Zindi is convening Africa’s data scientists to solve complex problems through data science. It is hosting online competitions on data challenges to help companies, NGOs and government organizations find solutions to complex business and administrative problems. 

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