Start your Data Science Journey with SQL – Four reasons why!

SQL (Structured Query Language) is a standard database language that is used to create, maintain, and retrieve relational databases. It is used to make the work of data retrieval, manipulation, and updation swift and easy. Started in the 1970s, SQL has become a very important tool in a data scientist’s toolbox (Read – Why every Data Scientist should know SQL?). But is SQL really needed for data science? Here are some reasons why the demand for SQL in the data science field is growing and why is it so important for every data scientist to learn SQL –


  • Easy to Learn – Learning SQL doesn’t require very high-level conceptual understanding and memorization of the steps. SQL is known for its ease of use which uses a set of declarative statements. The statements are structures in simple English language. Since data science, by its name, is all about the extraction of data and playing with it, there always comes a requirement for a tool that can fetch data from large databases easily. SQL is very handy at this.
  • Understanding the dataset – As a data scientist, you must master the understanding of the dataset you are working with. Learning SQL will surely give you an edge over others with less knowledge in the field. Data analysis using SQL is efficient and easy to do. SQL will help you to sufficiently investigate your dataset, visualize it, identify the structure, and get to know how your dataset actually looks.
  • Full Integration with Scripting Languages – As a data scientist, you will need to meticulously present your data in a way that is easily understood by your team or organization. SQL integrates very well with scripting languages like R and Python. SQL with python is widely used for data analysis.
  • Manages Large Data Warehouses – Data science in most cases involves dealing with huge volumes of data stored in relational databases. As the volume of datasets increase, it becomes untenable to use spreadsheets. The best solution for dealing with huge datasets is SQL techniques.

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Here are some handy tips, that a data scientist must follow to improve their SQL experience:

1) Data Modeling – Understanding relational data models is foundational to both effective analysis and using SQL. An effective data scientist should know how to model one-to-one, one-to-many, and many-to-many relationships. On top of that, they should be familiar with denormalized data models such as the star and snowflake schema.

2) Aggregations – Data analysis is all about aggregations. Understanding how the ‘group by’ clause interacts with joins and effective use of the ‘having’ clause for filtering will be foundational in working with large data sets.

3) Window Functions – Some of the most powerful functions within SQL, these unlock the ability to calculate moving averages, cumulative sums, and much more.

4) ‘IN’ Considered Harmful –  Almost every query that uses the ‘in’ operator can be rewritten using joins and subqueries for better performance. ‘IN’ is typically lazy query writing and should be avoided.

5) Navigating Metadata – You can easily navigate through query table structures, data types, index cardinality, etc. Very useful if you’re digging around a SQL terminal frequently.

Considering the scope of SQL in the field of data science and other industries, it becomes an essential skill that a data scientist must possess. For most data science jobs, proficiency in SQL ranks higher than the other programming languages. The ability to store, update, access control and manipulate datasets is a great skill for every data scientist. And due to the popularity of using SQL techniques in data science, there are innumerable online courses available for SQL learning. Every data scientist must begin their data science learning path with SQL as the first stepping stone.

Programming in SQL is highly marketable as compared to other programming languages.

Renowned Data Science Personalities

With the advancement of big data and artificial intelligence, the need for its efficient and ethical usage also grew. Prior to the AI boom, the main focus of companies was to find solutions for data storage and management. With the advancement of various frameworks, the focus has shifted to data processing and analytics which require knowledge of programming, mathematics, and statistics. In more popular terms, this process today is known as Data Science. Few names stand out and have a separate base of importance when the name data science comes into the picture, largely due to their contributions to this field and who have devoted their life and study to reinvent the wheel. Let’s talk about some of the best data scientists in the world.

Andrew Ng

Andrew Ng is one of the most prominent names among leaders in the fields of AI and Data Science. He is counted among the best machine learning and artificial intelligence experts in the world.  He is an adjunct professor at Stanford University and also the co-founder of Coursera. Formerly, he was the head of the AI unit in Baidu. He is also an enthusiast researcher, having authored and co-authored around 100 research papers on machine learning, AI, deep learning, robotics, and many more relevant fields. He is highly appreciated in the group of new practitioners and researchers in the field of data science. He has also worked in close collaboration with Google on their Google Brain project. He is the most popular data scientist with a vast number of followers on social media and other channels.

DJ Patil

The Data Science Man, DJ Patil, needs no introduction. He is one of the most famous data scientists in the world. He is one of the influencing personalities, not just in Data Science but around the world in general. He was the co-coiner of the term Data Science. He was the former Chief Data Scientist at the White House. He was also honored by being the former Head of Data Products, Chief Scientist, and Chief Security Officer at LinkedIn. He was the former Director of Strategy, Analytics, and Product / Distinguished Research Scientist at eBay Inc. The list just goes on.

DJ Patil is inarguably one of the top data scientists around the world. He received his PhD in Applied Mathematics from the ‘University of Maryland College Park’.

Kirk Borne

Kirk Borne has been the chief data scientist and the leading executive advisor at Booz Allen Hamilton since 2015. Working as a former NASA astrophysicist, he was part of many major projects. At the time of crisis, he was also called upon by the former President of the US to analyze data post the 9/11 attack on the WTC in an attempt to prevent further attacks. He is one of the top data scientists to follow with over 250K followers on Twitter.

Geoffrey Hinton

He is known for his astonishing work on Artificial Neural Networks. Geoffrey was the brain behind the ‘Backpropagation’ algorithm which is used to train deep neural networks. Currently, he leads the AI team at Google and simultaneously finds time for the ‘Computer Science’ department at the ‘University of Toronto’. His research group has done some overwhelming work for the resurgence of neural networks and deep learning.

Geoff coined the term ‘Dark Knowledge’.

Yoshua Bengio

Having worked with AT&T & MIT as a machine learning expert, Yoshua holds a Ph.D. in Computer Science from McGill University, Montreal. He is currently the Head of the Montreal Institute for Learning Algorithms (MILA) and also has been a professor at Université de Montréal for the past 24yrs.

Yann LeCun

Director of AI Research at Facebook, Yann has 14 registered US patents. He is also the founding director of NYU Center for Data Science. Yann has a PhD in Computer Science from Pierre and Marie Curie University. He’s also a professor of Computer Science, Neural Science and the Founding Director of the Data Science Center at New York University.

Peter Norvig

Peter Norvig is a co-Author of ‘Artificial Intelligence: A Modern Approach’ and ‘Paradigms of AI Programming: Case Studies in Common Lisp’, some insightful books for programming and artificial intelligence. Peter has close to 45 publications under his name. Currently the ‘Engineering Director’ at ‘Google’, he has worked on various roles in Computational Sciences at NASA for three years. Peter received his Ph.D. from the ‘University of California’ in ‘Computer Science.’

Alex “Sandy” Pentland

Named the ‘World’s Most Powerful Data Scientist’ by Forbes, Alex has been a professor at MIT for the past 31 years. He has also been a chief advisor at Nissan and Telefonica. Alex has co-founded many companies over the years some of which include Home, Sense Networks, Cogito Corp, and many more. Currently, he is on the board of Directors of the UN Global Partnership for Sustainable Data Development.

These are some of the few leaders from a vast community of leaders. There are many unnamed leaders whose work is the reason why you have recommender systems, advanced neural networks, fraud detection algorithms, and many other intelligent systems that we seek help to fulfill our daily needs.

Tableau vs PowerBI: 10 Big Differences

The concept of using pictures to understand patterns in data has been around for centuries. From existing in the form of graphs and maps in the 17th century to the invention of the pie chart in the mid-1800s, the idea has been exquisite. The 19th century witnessed one of the most cited examples of data visualization when Charles Minard mapped Napoleon’s invasion of Russia. The map depicted the size of Napoleon’s army along with the path of Napoleon’s retreat from the city of Moscow – and tied that information to temperature and time scales for a more in-depth understanding of the event.

Read more about data Visualisation in our previous blog – Practices on Data Visualisation.

In the modern world, when it comes to the search for a Business Intelligence (BI) or Data Visualisation tool, we come across two front runners. They are PowerBI and Tableau. These are the top data visualization tools. Both of these products are equipped with a set of handy features like drag-and-drop, data preparation amongst many others. Although similar, each comes with its particular set of strengths and weaknesses, and hence very often articles titled Tableau vs PowerBI are encountered. The following comparisons provide insights into which data visualization tool is best for different purposes.

The tools will be compared on the following grounds:

  • Cost
  • Licensing
  • Visualization
  • Integrations
  • Implementation
  • Data Analysis
  • Functionality

Cost remains a significant parameter when these products are compared. This is because at one end PowerBI is priced around 100$ a year while Tableau can be rather expensive up to 1000$ a year. PowerBI is more affordable and economical than Tableau and is suitable for small businesses. Tableau, on the other hand, is built for data analysts and offers in-depth insight features. So, when it comes to Tableau vs PowerBI cost comparison, Tableau is a better alternative to PowerBI.

Tableau should be the first choice in this case. To explain why Tableau over PowerBI, the final choice is considered that is, whether one wants to pay upfront cost for the software or not. If yes, then Tableau should be chosen else one should opt for PowerBI.

When it comes to visualization features, both the products have their strengths. PowerBI can prove to be better if the desired outcome is data with better visuals. PowerBI lets you easily upload datasets. It gives a clear and elegant visualization. However, if the prime focus is visualization, Tableau leads by a fair margin. Tableau performs better with more massive datasets and gives users efficient drill-down features.

PowerBI has API access and pre-built dashboards for speedy insights for some of the most widely used technologies and tools like Salesforce, Google Analytics, and Microsoft Products. On the contrary, Tableau has invested heavily in integrations and widely-used connections. A user can view all of the connections included right when he/she logs into the tool.

This parameter along with maintenance is primarily dependent on factors like the size of the company, the number of users, and others. Power BI comes out to be fairly more straightforward on the grounds of implementation and requires a low level of expertise. However, Tableau, although is a little more complex, offers more variety. Tableau incorporates the use of quick-start applications for deploying small scale applications.

Data Analysis
Power BI with Excel offers speed and efficiency and establishes relationships between data sources. On the other hand, Tableau provides more extensive features and helps the user in hypothesizing data better.

For the foreseeable future, any organization which has users spending more than an hour or two per day using their Business Intelligence tool might want to go with Tableau. Tableau offers a lot of features and minor details that are unmatched.

Feature Power BI Tableau
Date Established 2013 2003
Best Use Case Dashboards & Ad-hoc Analysis Dashboards & Ad-hoc Analysis
Best Users Average Joe/Jane Analysts
Licensing Subscription Subscription
Desktop Version Free $70/user/month
Investment Required Low High
Overall Functionality Very Good Very Good
Visualisations Good Very Good
Performance With Large Datasets Good Very Good
Support Level Low (Or through partner) High

It all depends upon who will be using these tools. Microsoft powered Power BI is built for the joint stakeholder, not necessarily for data analyticsThe interface relies on drag and drop and intuitive features to help teams develop their visualizations. It’s a great addition to any organization that needs data analysis without getting a degree in data analysis or any organization having smaller funds.

Tableau is more powerful, but the interface isn’t quite as intuitive, which makes it more challenging to use and learn. It requires some experience and practice to have control over the product. Once this is achieved, Tableau is better than PowerBI and can prove to be much more powerful for data analytics in the long run.

Data Visualization: 6 Best Practices

Our world is progressively filling up with data, all companies – significant multinationals to the minor young startups are stockpiling massive amounts of data and are looking for ways to analyse this data in the raw form and obtain processed information, that can make complete sense. Data Visualisations represent data in pictorial form for the marketing managers to understand complex data diggings.

According to a fact, 3.5 trillions of e-mails are sent every day for the promotion of many companies; companies prepare ads, stockpile enough resources to deliver them to as many users as they can. With a slight observation, a considerable portion of receivers can be cut-off, who have a meagre – conversion rate. Doing so will not only lower the wastage of their resources but will also help them concentrate more on the people belonging to a higher rate of conversion, thus increasing the chances of the product being sold. For doing this, the implementation of supreme data visualisation is necessary.

Data Visualisation can take everyone by surprise. It is here that a meaningless looking pile of data starts making sense and delivers a specific result as per the likes of the end user or developer. It takes shape with the combined effort of ones creativity, attention, knowledge, and thinking. Data Visualisation can be useful, as well as harmful. (Read: 5 common mistakes that lead to Bad Data Visualization)To help your cause by not misleading your visualisation, here are some of the best practices for making your visualisation clear, useful and productive.

A. Plan your Resources
Create a sequence of steps by obtaining your requirements, your raw data, and other factors that might affect the result. This requires knowledge and experience for a data scientist to choose which method to use to for visualising your data. Planning the resources can be very helpful, as it will lead to greater efficiency with the efficient workload.

B. Know your Audience
The most essential and unavoidable step in creating great visualisations is knowing what to deliver. Focus on the likes of the audience, their mindsets, their queries, their perceptions and beliefs; and then plan effectively. It is not necessary that all the viewers will receive the information in the same way. For example, a probability density graph has a different meaning for an HR manager and a chief sales executive. So, it’s very vital that you know your target audience and prepare visualisations according to their perspective.

C: Predict after-effects
Predicting what might be the effect on the end users can add up to your cause. There can be a no-action session where everything is going positive in your way, while a downfall in a particular field may require some immediate action.

D: Classify your Dashboard
There are three main types of dashboards – strategic, analytical and operational. Following the below steps would let you know which dashboard suits best.

  • Strategic Dashboard: It represents a top notch level view of the inquiry line answered in a daily specific routine and presents KPIs in a minimally interactive way.
  • Analytical Dashboard: It provides a range of investigative approaches to a central specific topic.
  • Operational dashboard: It provides a regularly updated answer to a line of enquiry based on response to events.

E: Identify the Type of data

  • Data is of three types: categorical, ordinal and quantitative. Different types of visualisation work better with different kinds of data. A single relation of a data works best with line plot, two pieces of data work better with a scatter plot. A brief description of the type of data is given below:
    • Quantitative: Defines the number of data
    • Ordinal: Data that belongs to the same sequence. Ex: Medals – Gold, Silver and Bronze.
    • Categorical: Data that is of one type. Ex: Gender – Male, female and Other.

F: Use of Visual Features

  • Having done the above, a perfect choice of colour, hue, saturation can glorify your visualisation. It is just a matter of the presence of mind that draws attention.
  • Using the wrong hue and saturation configurations can bring ruin to all your efforts. A good set of visual features gives a final touch up to your data visualisation.

Create some stunning reports and real time dashboards with Xaltius’ BI and Analytics Services.

Concluding, modern technologies like machine learning and AI by itself will find no use for business corporates, if not for data visualisation. Data Visualisation has itself found its field of study and interests and finds its importance in every walk of analysing data.

Artificial Intelligence – Transforming Human Resource

The fourth industrial revolution is bringing forward transformation powered by artificial intelligence in many sectors. The Human Resource department is no less affected.  While AI cannot replace the HR department as a whole, it can certainly bring forth massive improvement. Experts point out that many innovative methods using AI can be adopted to minimize unnecessary work load, while maximizing employee selection and their efficiency.  Machines can takeover tasks that are tedious and time consuming. It can also bring transparency and accuracy to many processes that are usually subject to discrimination. Thus, it would help make the process better and take informed decisions.

Data management and analytics

The data collected by the HR department of the corporates can be effectively managed using AI. Face-recognition and other technologies that are capable of identifying gender and measuring employees’ psychological and emotional traits can be used and the data generated can be used for analytics. Each employee’s performance can be analyzed in depth so that their employees will have a clear picture on who to keep and who to let go. Evaluating the workforce can bring about smarter decisions that will lead to better performance results.

Analyzing such data can predict the future ROI, increase or reduce engagement levels of employees, solve problems pertaining to completion of projects and other unforeseen glitches that would normally go un-noticed by the human eye. It can also provide insights to employees on how to work more efficiently.

The hiring process

Talent acquisition is one of the major areas where AI can be a blessing. An analysis of the resumes can put forward the best candidate for the job, with the algorithm giving importance to the factors that the company wants. Focusing on performance, culture and career-alignment analysis, AI can quickly identify whether or not a candidate is a good fit. AI will also be devoid of the biases based on race, gender or other factors that usually influence the process.

Replacing Administrative Tasks

Repetitive recruiting tasks such as sourcing resumes, scheduling interviews and providing feedback can be replaced by machines, giving the officials time to work on other matters. Conversational interfaces can be used instead of emails for communication. Chatbots can be used to answer real-time questions raised by either the employees or the customers.

AI can never replace this human driven sector completely, which places so much importance on personal relationships. AI will most likely never replace processes which involve connecting with top talent, providing a more personalized interview experience and establishing training and mentoring programs. In lieu of the above, Ben Peterson from BambooHR say that “increasing speed, quality and efficiency without sacrificing meaningful communication and relationships seem to be the right balance leading to the best possible outcome.”

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

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