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.

A Short History of Data Science

Over the past two decades, tremendous progress has been made in the field of Information & Technology. There has been an exponential growth in technology and machines. Data and Analytics have become one of the most commonly used words since the past decade. As they are interrelated, it becomes essential to know what is the relation between them and how are they evolving and reshaping businesses.

Data Science was officially accepted as a study since the year 2011; the different or related names were being used since 1962.

There are six stages in which the development of Data Science can be summarised-

Stage 1: Contemplating about the power of Data
This stage witnessed the uprising of the data warehouse where the business and transactions were centralised into a vast repository. This period was embarked at the beginning of the 1960s. In 1962, John Tukey published the article “The Future of Data Analysis” – a source that established a relation between statistics and data analysis. In 1974, another data enthusiast, namely Peter Naur, gained popularity for his article namely “Concise Survey of Computer Methods”. He further coined the term “Data Science” which came into existence as a vast field with lot many applications in the 21^st century.

Stage 2: More research on the importance of data
This period was witnessed as a period where businesses started research for finding the importance of collecting vast data. In 1977, the International Association of Statistical Computing (IASC) was founded. In the same year, Tukey published his second major work – “Exploratory Data Analysis” – arguing that emphasis should be laid on using data to suggest the hypothesis for testing and simultaneous exploratory testing for confirmatory data analysis. The year 1989 saw the establishment of the first workshop on Data Discovery which was titled Knowledge Discovery in Databases(KDD) which is now more popularly known as the annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD).

Stage 3: Data Science gained attention
The early forms of markets began to appear during this phase. Data Science started attracting the attention of businesses. The idea of analysing data was sold and popularised. The Business Week cover story from the year 1994 which was titled ‘Database Marketing” supports this uprise. Businesses started to witness the importance of collecting and applying data for their profit. Various companies started stockpiling massive amounts of data. However, they didn’t know what and how to use it for their benefit. This led to the beginning of a new era in the history of Data Science.

The term Data Science was yet again taken in 1996 in the International Federation of Classification Societies(IFCS) in Kobe, Japan. In the same year, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth published “From Data Mining to Knowledge Discovery in Databases”. They described Data Mining and stated “Data mining is the application of specific algorithms for extracting patterns from data.

The additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, became essential to ensure that useful knowledge is derived from the data.

Stage 4: Data Science started being practised
The dawn of the 21^st century saw significant developments in the history of data science. Throughout the 2000s, various academic journals began to recognise data science as an emerging discipline. Data science and big data seemed to work ideally with the developing technology. Another notable figure who contributed largely to this field is William S. Cleveland. He co-edited Tukey’s collected works, developed valuable statistical methods, and published the paper “Data Science: An Action Plan for Expanding the Technical Areas of the field of Statistics”.

Cleveland put forward his notion that data science was an independent discipline and named six areas where data scientists should be educated namely multidisciplinary investigations, models and methods of data, computing with data, pedagogy, tool evaluation, and theory.

Stage 5: A New Era of Data Science
Till now, the world has seen enough of the advantages of analysing data. The term data scientist is attributed to Jeff Hammerbacher and DJ Patil as they carefully chose the word. A buzzword was born. The term “data science” wasn’t prevalent yet, but was made incredibly useful and significantly developed. In 2013, IBM shared the statistics that 90% of the world’s data has been created in the last two years alone. By this time, companies had also begun to view data as a commodity upon which they could capitalise. The importance of transforming large clusters of data into usable information and finding usable patterns gained emphasis.

Stage 6: Data Science in Demand
The major tech giants saw significant developments in demand for their products after applying data science. Apple laid out a statement for increased sales giving credit to BigData, and Data Mining. Amazon said that it sold more Kindle online books than ever. Companies like Google, Microsoft used deep Learning for speech and Voice Recognition. Using AI techniques, the usage of data was further enhanced. Data became so precious; companies started collecting all kinds of data from all sorts of sources.

Putting it all together, data science didn’t have a very prestigious beginning and was ignored by the researchers, but once its importance was adequately understood by the researchers and the businessmen, it helped them gain a large amount of profit.

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.

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.

{DATA SCIENCE AND WEB DEVELOPMENT}

Data Science and Python, though not a lot of people may realize it, go hand in hand with each other. Businesses today, especially the higher level management, require to see accurate and efficient depictions of various data science solutions and projects. Knowing both bridges that gap considerably.

Xaltius took to imparting the fundamentals of both these areas to over 150 students at NUS Business School over an 8 hour, hands-on intensive seminar and workshop.

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Through the workshop the keys takeaways for the students were:

Understand the fundamentals of Python and working with small datasets.
How to create basic data visualizations through seaborn in python.
How to tell a story about your data, which is one of the most important lessons.
Basics of HTML, CSS and JavaScript which would help users learn about how to create basic web pages.

The end of the workshop ended by doing a small hack with the students where they were given data and had to tell a story around it. They were given an opportunity to present their findings and many of did amazingly well in such a short period!

If you are interested conduct such workshops and talks for your institution or be part of one, please get in touch with us.