Data science most valuable and expensive entity in the 21st century is not Gold or Oil or Diamonds, but Data. The science revolving around information, more commonly known as Data Science, Analytics around data, and Machine learning is growing and evolving at an exponential rate.
Professionals who can see through this vast sea of data and help organize it in a manner that can be beneficial to a company are considered as the biggest assets to an organization. The farm of data if harvested efficiently, can help reap profits of the highest order for an organization.
Why Data Science?
For over a decade, humans have been trying to best define data science. Hugh Conway created a Venn diagram in 2010, consisting of 3 circles that help understand data science in the best possible manner. The three circles represent the following fields of knowledge.
- Math and Statistics
- Subject Knowledge (which is knowledge of the domain under observation)
- Hacking skills
The intersection of these 3 circles lead to the zone that accurately represents the field of data science. If an individual has expertise of all these 3 skills, it can be concluded that they are highly proficient in data science.
Data Science is a process where a huge amount of data is sorted by cleaning it, organizing it, and then finally analyzing it to see if it can be useful. Data is available from various sources and what a data scientist does is collect it from the available sources and then apply several external factors to it such as predictive analysis, machine learning, sentiment analysis, etc. to retrieve data from these data sets which is of critical importance. The next objective of a data scientist is to understand this extracted data from the point of view of the business requirement and convert it into accurate insights and predictions that can eventually be used to power the decisions that are to be taken for the given business.
What should know a Become Data Scientist?
Ideally, any individual who is looking to build a career in the field of data science should be efficient with skills and tools that will help them in the following three departments.
- Domain knowledge
This is a broad classification of what is required of a data scientist. If you dive another level deeper, the skills listed down below will carve out the essentials of a data scientist in an individual.
- Very Good knowledge of programming languages like Scala, R, Python, SAS
- Proficiency and hands-on experience in SQL databases
- Ability to collect and sort data from unstructured and unorganized sources like digital media and social media
- Understanding of various analytical functions
- Knowledge and curiosity about machine learning
Who is a Data Analyst?
A data analyst can be defined as an individual who can provide statistics that are basic and descriptive in nature, visualize and interpret data, and then convert it into data points so that a conclusion of that data can be drawn.
It is as assumed and expected that a data analyst has an understanding of statistics, has some or very good knowledge about databases, can create new views, and has the perception that is required to visualize data. It can be accepted that data analytics is the primary form of data science which is considered to be much deeper and more evolved.
What does a data scientist do?
It is imperative that a data analyst has the ability to take a particular topic or a particular question and should be able to put forward the raw data in a format that can be understood comfortably by the stakeholders in a given company. The four key skills listed below are essential if you are looking to jump into the path that leads to becoming a data analyst.
• Thorough knowledge of mathematical statistics
• Fluency in understanding programming languages such as R and Python
• Understanding of PIG/HIVE
• Data Wrangling
How does Machine Learning work?
We can define machine learning as the practice of creating and implementing algorithms to use the data that we have at hand, learn from that data and forecast and predict future trends for a given topic. Machine learning traditionally revolves around statistical analysis coupled with predictive analysis to understand patterns in data sets that can help us identify insights that are usually hidden in the data that is collected. Let us try and understand this in simple terms with the help of an example.
Let us understand how machine learning has been implemented in the most popular social media website in the world today, Facebook. Facebook has machine learning algorithms which constantly study a user’s behavior on the website. Based on the past behavior, the algorithm understands the nature of a user, which helps it know about a user’s interests.
It studies a user’s past behavior based on what the user has liked in the past, which pages the user has followed, and then predicts what other articles of similar interest would be relevant to the user and displays it in from of them on their news feed. This is similar to Amazon where in when a user purchases a product, Amazon’s algorithms quickly suggests other relevant products that the user may want to buy.
Another good example on machine learning is when you watch Netflix and based on the kind of movies a user has watched in the past, Netflix starts suggest relevant movies belonging to the same genre on the user’s home page.
What are the Skills Need to Become a Machine Learning Expert
Machine learning is just a digital approach to statistics. To carve out a career in the Machine Learning domain, the following skills are considered to be essential.
- Knowledge of computer fundamentals
- Expertise in programming skills
- Experience in probability and statistics
- Evaluation skills and data modeling
Machine Learning and Data Science Intersect?
We have already established that data science is a superset, which consists of various disciplines, and we can say that even machine learning is a subset of data science. Processes such as clustering and regression are used in the field of machine learning. While on the other hand, it is not necessary that the data in the data science requires backing from a machine or a process that is mechanical.
The main point of differentiating between the two is that data science is a broader aspect, which not only looks at statistics and algorithms but also looks at the complete method of processing the data. We can therefore say and believe that data science is an amalgamation of several disciplines, which include software engineering, machine learning, data analysis, data engineering, business analytics, predictive analysis, and more.
Five Important Considerations in Data Science
Data science is growing very prominently as a tool of strategic business. It has been found out in a study by Deloitte that many organizations are looking to triple their team of data scientists within the next 24 months.
With the current implementations of GDPR and privacy being under scrutiny more than ever, data scientists have made it important to model and process data in a responsible manner. The following five considerations are what data scientists are looking at in the months to come
- .Explainability and Transparency
- Version Control
- Data as the new IP
- Data Bias
- Data Aggregation
Explainability and transparency
May 2016 saw the introduction of General Data Protection Regulation (GDPR), which paved a change in the manner global organizations would collect, manage and process data of people belonging to the European Union. This resulted in an impact in the field of data science as it made it important to think about what kind of data can be used by data science for its modeling purposes and how transparent would the models need to be.
As per GDPR compliance an organization should be able to justify how they arrived at a decision that was based on data models. This implies that any organization must secure all the data that they have on a customer and should have sufficient consent from the customer if they want to use that data.
It is also expected that in the coming years, regulations around ePrivacy could get a lot harsher which will have an impact on how data can be used. Data architecture will be the next real challenge for data scientists such that it stays in compliance with the regulations made by the law makers.
Version control for data is closely associated with GDPR and ePrivacy. Changes being made to software and data by you or other people working on the project is very critical to the project.
Why is this important? Because as a data scientist, when you are explaining the outcome that led to a conclusion based on a data model in agiven point in time, you may sometimes need to refer to an earlier iteration of the data.
This is really important if you are building models that goes through build change frequently or partially until it reaches the latest build, it is important to store both historic and current builds of the data in the event of an audit.
This holds true in the case where you are running frequent iterations on development of models. Model development is a process that goes through iterations, wherein new packages and techniques are being made available with each iteration.
Business should be attentive to their complete suite of models and not just the new models. Versioning should be given importance and implemented so as to be in compliance at all given times.
Whether you are a person who makes changes and maintains them manually, or you use version control software like Git, or you are outsourcing version control, you need to ensure that version control is your priority as a data scientist. Failing to do so will put you and your work at risk and can result in the wrath of an Information Commissioner who may even fine you heavily.
Data as the new IP
There is a theory that data is becoming the new IP because along with the code in a software, data is as important now while creating models that are proprietary. The standard of using open source software is growing and computer resources are therefore becoming more affordable.
This means that many more enterprises can now build software without a very high budget. The availability of quality and volume in training data is what differentiates models. This holds true for both in industries which are just adapting to the new market and are generally slow and static where the data is sparse, and fast-moving industries where data is retrained frequently.
If you look at data giants like Google and Amazon, you will understand that training data is quickly becoming an intellectual property and something that gives one company a competitive advantage over another.
Model retraining using automation is all well and good. There is a problem, however, which is that of human bias, a problem that is supposed to be eliminated using algorithms and machine learning.
Human bias can be passed to a machine when a machine is being trained in the event that the data being fed to the machine contains traces of a bias. For example, consider a finance industry.
If the data being fed is biased, the results may end up offending the fair lending act known as the Equal Credit Opportunity Act. As we have learned from the GDPR act, a customer has the right to know about how a decision was reached; if a loan was rejected, and if this decision was reached due to a bias of data, the case could become difficult to be justified to the customer.
We have seen a number of data sets where speech recognition models could not recognize regional accents and image recognition models returned results that were racist in nature, all because the data used to train the models was skewed and biased in nature.
GDPR states that anonymity should be ensured by aggregating customer data to a group size that is specific in nature. This does feel like something that would put restrictions on maintaining data but we could also look at it as an opportunity to put more creativity into the thought process that goes into building models and how they would be of benefit to the consumer.
Innovation in techniques in clustering and feature generation of data would mean that we will be able to understand and recognize patterns in data and information that were not seen previously. Instead of just trying to comply with GDPR, we could use this as an opportunity to create new models and techniques that will be more customer centric.
Data science has reached a state of its development cycle that is very interesting. There is something new happening every day and newer possibilities are being introduced that can be afforded by the discipline. We should also focus on how we can appreciate privacy of data and that it is the responsibility of data scientists to train machines to respect the data of consumers whose data is being used.
Top 10 library for Data Science
The reaction between the huge volume of data that is available to organizations and how they can put it to use in their decision making such that the organization benefits from this data is what resulted in the need for data science in today’s world.
The absence of proper tools is all that stands as an obstacle to determine the value that all this information and data has in determining the economic and social value of this data for an organization.
Data science came into existence to fill this void of tools to analyze and use this huge set of data. For a business to grow at a constant and good rate, for it to develop, inputs are required which will allow the business to manufacture and produce a product that is required by the consumer.
Data science teams come into the picture to develop these specific needs of a growing business. When the general population gives intimate feedback to the models built by a data science team, you can say that their purpose is achieved.
When it comes to analytics of entities such as cloud processing, machine learning, neural systems, image processing and so on, MATLAB is the go-to software for many data scientists which is a platform that is very simple and easy to understand and get a grasp on. Huge amounts of data coming from multiple sources can be analyzed with the help of MATLAB.
The versatile nature of MATLAB gives it a range from telematics, sensor analytics, all the way to predictive analysis. With the help of MATLAB, data from various sources such as web content, video, images, sound, record frameworks, IoT gadgets, etc. can all be analyzed. MATLAB offers a 1-month free trial and provides annual licenses beginning from USD 820 per year.
Multiple enterprises deploy TIBO Statistica to understand and solve their numerous issues that are unpredictable in nature. The platform allows users to assemble different models built by them allowing for refreshed learning, analytical procedures, artificial intelligence, and so on. With the help of TIBCO Statistica, one can create complex level of algorithms such as clustering, neural systems, machine learning, all which are accessible via a few nodes.
A California-based software company is the creator of Alteryx Analytics. Business intelligence and predictive analytics products are the primary offerings of this software company that are used for processes related to data science and analytics. The annual membership starts from USD 3995.00 per year and their cloud-based software suite starts at pricing of USD 1950.00 per year. Data giants like Amazon Web Services, Microsoft, Tableau, and Qlik are partners with Asterix Analytics. the go-to software for many data scientists which is a platform that is very simple and easy to understand and get a grasp on.
Huge amounts of data coming from multiple sources can be analyzed with the help of MATLAB. The versatile nature of MATLAB gives it a range from telematics, sensor analytics, all the way to predictive analysis. With the help of MATLAB, data from various sources such as web content, video, images, sound, record frameworks, IoT gadgets, etc. can all be analyzed. MATLAB offers a 1-month free trial and provides annual licenses beginning from USD 820 per year.
TIBCO Statistica Multiple enterprises deploy TIBO Statistica to understand and solve their numerous issues that are unpredictable in nature. The platform allows users to assemble different models built by them allowing for refreshed learning, analytical procedures, artificial intelligence, and so on. With the help of TIBCO Statistica, one can create complex level of algorithms such as clustering, neural systems, machine learning, all which are accessible via a few nodes.
Alteryx Analytics A California based software company is the creator of Alteryx Analytics. Business intelligence and predictive analytics products are the primary offerings of this software company that are used for processes related to data science and analytics.
The annual membership starts from USD 3995.00 per year and their cloud-based software suite starts at a pricing of USD 1950.00 per year. Data giants like Amazon Web Services, Microsoft, Tableau and Qlik are partners with Asteryx Analytics.
RapidMiner Studio is a software that is considered to be a visual workflow designer. A tool that helps with processes such as data preparation, machine learning, text mining and predictive analytics, it was specifically developed to help make the lives of data scientists easy.
Using the RapidMiner Turbo prep, data scientists can do pivots, take charge of transforming data as well as blend data collected from various sources. All these transactions surprisingly can be processes using a minimal number of clicks.
Databricks Unified Analytics Platform
The creators of Apache Spark created the Databricks Unified Analytics Platform. It provides shared notepads and an environment where users can coordinate to tackle and work with a majority of tasks that fall under analytical procedures. Data scientists can create applications working on artificial intelligence and also create new models consistently. The software is available as a trial for a 14-day period.
With a total number of over seven million users all over the world, Anaconda is a software that is free and open source. Anaconda Distribution and Anaconda Enterprise are the most popular products available from this open-source package. The Anaconda distribution empowers data scientists with a platform and environment that supports around 2000 data bundles in the programming suite of Python and R language for Data Science.
Used by industries such as finance, healthcare, retail, manufacturing, telco, etc. H2O brags of a user base of 155,000 users in over 14000 organizations worldwide. Driverless AI, which is one of the tools offered by H2O, made it to the winner’s list of the 2018 InfoWorld Technology Awards. Multi-million-dollar organizations such as PayPal, Dun and Bradstreet, Cisco, and a few more businesses working in assembly use H2O packages very prominently.
KNIME Analytics Platform
The KNIME Analytics Platform is a software that is open source again. Machine learning algorithms and advanced predictive algorithms that use end-to-end workflow in data science are powered by the KNIME Analytics Platform. This software makes it convenient to retrieve data from sources such as Google, Azure, Twitter and Amazon Web Services S3 buckets.
R programming language clients utilize the R-Studio tool as an Integrated Development Environment. The R-Studio platform is very intelligent and furthermore contains bundles that are built-in, which can be used for graphics and computing data that is statistical in nature. The R-Studio platform is supported by all major operating systems such as Windows, Linux and MAC.
Cloudera Data Science Workbench
Among all the platforms that are available to data scientists, software engineers and programming specialists in the world today, Cloudera Data Science Workbench is the most loved platform by all. The tool contains the latest and most updated libraries scripted in languages such as Python,
Scala and R, which can be utilized by end-users and data scientists. Data scientists and users have the liberty to develop and create their machine learning models with just a few clicks and hauls which is very comfortable and convenient as compared to all other available platforms.