Prerequisites for Learning Data Science and Finding Relevant Jobs

Data Science is a multidisciplinary blend of data inference, algorithm development, visualisations, and technology in order to solve analytically complex and challenging problems. At the core is data (with raw information) and it has to be processed.

With the rise of big data comes the need for more highly skilled people to mine and interpret that data for businesses. I classify them into two types -

TYPE 1: Individuals without coding background looking for data science opportunities with limited coding

TYPE 2: Individuals with/without coding background, looking for Data Science Opportunities with a large amount of coding

The details are expressed below:

TYPE 1: For individuals without coding background looking for data science opportunities with limited coding

1. Familiarity with mathematics and statistics

One should be good at mathematics, statistics and possess an analytical aptitude.

2. A good understanding of Data Analysis Life Cycle (DALC)

Data Analysis Life Cycle (DALC) is a process to understand the data and apply mathematics/statistics to get insights for a business objective. It is the same way that we do in Software Development Life Cycle (SDLC) model, if the requirement is not clear, then we might develop or test the software wrongly.

3. Basic understanding of SQL and knowledge of databases

It's important to know how to write a basic SQL query and having familiarity with joins, group by, having, creating indexes, etc. to get the data out, for analysis. Regardless of whether the data is to be retrieved from a database.

4. Basic knowledge of using Analytic Tools (limited or non-programming)

There are many non-programming analytic tools such as Tableau, Data Wrapper, Google Charts, Qlik, Mondrian etc. It is worth to know how data analysis can be carried out.

5. Basic understanding of Data Operations, Visualisation & Reporting

• Data collection may be a tedious task especially when carried out with specific instructions. If can manually recording or automated.
• Data cleaning includes removing and replacing junk data, filling in some gaps if present.
• Data munging (data wrangling) is the process of converting the raw form of data into a form that is convenient to study, easy to analyse, and comfortable to visualise. It is also called as data transformation, which consists of transforming the data as per our requirement (business requirements) to achieve the objective.
• Visualisation of data and its presentation are an equally important set of skills on which a data scientist relies heavy when facilitating managerial and administrative decisions using his/her data analysis.

6. Passion to develop business acumen

Some of the soft skills you need to develop or work on include business acumen (to put to good use the insights you have uncovered for business growth), communication and visualization skills (to make laymen understand about your insights to convince them for crucial business decisions), problem solving skills, intuitive skills, creativity and industry knowledge.

Though Data Scientists work with high-end technologies, the beneficiaries are the business as a whole. The primary business expectation from a Data Scientist is a reliable IT system that delivers data-driven decisions for day-to-day business problems. So, when thinking of businesses, one has to keep in mind that the aim of a business solution is not to showcase technology, but to solve the identified business solution.

7. Good data intuition

The data intuition means perceiving patterns where none are observable on the surface and knowing the presence of where the value lies in the unexplored pile of data bits. This makes data scientists more efficient in their work. This is a skill which comes with experience and boot camps are a great way of polishing it.  

8. Intellectual Curiosity

• Curious to play with data
• Curious to work with different data formats like XML, XLSX, CSV & JSON
• Curious to gain knowledge on analytics and how it assists with decision making

TYPE 2: For Individuals with/without coding background, looking for Data Science Opportunities with a large amount of coding

The points mentioned for the data science opportunities with limited coding also holds good for a large amount of coding but one needs to be strong in mathematics, statistics, and programming.

1. Strong in mathematics and statistics

Statistical analysis and the knowledge to leverage the power of mathematics and computing frameworks to mine, process, and present the value out of the unstructured data is the most important technical skill required to become a data scientist.  

2. Strong in Programming

It's vital to know the basic concepts of object-oriented programming like C, C++ or Java to ease the process of learning data science programming tools like Python and R.

3. Strong in SQL, Databases and various technology integrations

Given the huge amount of data generated virtually every minute, most industries employ database management software such as MySQL, Cassandra etc. to store and analyse data. A good insight towards the working of DBMS will surely go a long way in data science jobs. So, it is good to be technically strong in SQL databases, database design, data mining, data munging and cleaning.

There are many technologies that are emerging for SQL interfacing with Hadoop so for a data scientist to know how to write a Hadoop MapReduce job is not necessary. Knowledge of basic distributed system concepts like MapReduce, Pig, Hive would be helpful but again it depends on which company you will be working for. Many companies have started using Hadoop-as-a-Service so data scientists need not have an in-depth working knowledge of Hadoop.

Hadoop Platform – having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial.

The other requirements could be:

• Deep understanding of Data mining/analysis and Statistics, along with business perspectives and cutting-edge practices using SPSS, SAS, R, Python, Hive, Spark and Tableau.
• Good understanding of machine learning algorithms in R and/or python.
• Good knowledge of deep learning and artificial intelligence (using various libraries such as TensorFlow, Theano, Torch, scikit-learn, Caffe etc.
• Get complete knowledge of tools and techniques for data transformation
• Work with Hadoop Mappers and Reducers to analyse data.
• Ability to work with unstructured data, whether it is from social media, video feeds or audio.

Resources & Information

1.    Advanced Degree – More Data Science programs are popping up to serve the current demand, but there are also many Mathematics, Statistics, and Computer Science programs.

2.    MOOCs –Coursera, Udacity, and codeacademy are good places to start.

3.    Certifications – KDnuggets has compiled an extensive list.

4.    Bootcamps – For more information about how this approach compares to degree programs or MOOCs, check out this guest blog from the data scientists at Datascope Analytics.

5.    Kaggle – Kaggle hosts data science competitions where you can practice, hone your skills with messy, real world data, and tackle actual business problems. Employers take Kaggle rankings seriously, as they can be seen as relevant, hands-on project work.

6.    LinkedIn Groups – Join relevant groups to interact with other members of the data science community.

7.    Data Science Central and KDnuggets – Data Science Central and KDnuggets are good resources for staying at the forefront of industry trends in data science.

8.    Follow Data Science websites - such as Kdnuggets, datascience101, DataTau etc. to remain in sync with the happenings of the World of Data Science and gain an insight regarding the type of job openings currently being offered in the field.

9.    For more Data Science related articles, read Challenges and Applications in Data Science.