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.

Data Science in the Chemical Industry

Data science and analytics is such an evergreen field that finds its use in every industry. Today the world is moving towards automation, and even the chemical industry is starting to adopt such practices and thus the use of data science in the chemical industry has increased significantly. Every experiment starts from a simulation of a process in the laboratory and data science and modeling helps in scaling it from the lab scale to a plant scale. So, let us dive deep into understanding how data science can be applied to chemical engineering.

For example, a lot of times, the chemical industry is full of recording errors. Error in recording parameters may hamper various simulations and processes. In such cases, data science and analytics in the chemical industry provides a significant advantage. A few major advantages of using industrial data science techniques are:

  • It helps in quickly identifying trends and patterns, which is an essential requirement for the chemical industry to recheck an observation.
  • It leads to reduced human effort, which means fewer chances of errors and reduced cost.
  • As data Science handles multi-dimensional and multi-variety data, things can be done in a dynamic and uncertain environment.
  • Observing calculations to estimating the number of chemicals required for a reaction, holds the capacity to benefit the industry.

Considering the above points in mind, we can clearly state that analytics can not only boost production but can also reduce and cut-off unprofitable production lines that are not of any use, helping in both – reduced energy consumption and reduced wastage of valuable resources like labor and time.

Stan Higgins, the retired CEO of the North East of England Process Industry Cluster (NEPIC), who currently is a non-executive director at the Industrial Technology Systems (ITS) and also a senior adviser to Tradebe, which is waste management and specialty chemical company, says that miracles can be done using analytics in chemical industry. He describes that his work accompanied by data analytics led him to win the Officer of the Order of the British Empire (OBE) for the work promoting the UK’s process manufacturing industry. He describes that in production, the challenges are never-ending.

 

The key to any successful venture is maintaining quality production and maximizing output within health, safety, and environmental goals. Every day, new chemicals, and intermediates are being developed in chemical industries, and it requires a lot of attention for a human being, considering all processes like cost, availability, quantity, and then being able to decide the most suitable chemical product and alternative on a daily basis. The chances of error are very high, and it can be crucial to the industry.

What are some of the other uses of data science and analytics in the chemical industry?

  • Use for checking the overall value of an alternative chemical, over the currently being used chemical.
  • It can help in determining precise and essential measurements for the reactivity of chemicals, checking for their optimum conditions that are favorable.
  • It can help in understanding the best reactivity of a catalyst for the different conditions of temperature, pressure, and other conditions.
  • It helps in guessing a pre-determined result after a reaction.

Concluding, it won’t be inappropriate to say that there isn’t a field where data science and analytics can’t find its application. For large industries, business intelligence plays a key role in promoting growth. So, analytics and BI in chemical industries can bring about huge improvements over a period of time.

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