What’s missing from all Data Science training programs?

Data Science profiles have grown over 400 times in the past year as quoted by ‘The Economic Times’. With the coming up of thousands of start-ups, the demand for data scientists has shot up in India as well. Data science is indeed one of the sexiest careers of the 21st century and Data Scientists are therefore the rock stars of this era.

But what does it takes to get into the data science field?

Apart from having a degree in mathematics/statistics or engineering, data scientists need to be technically proficient in programming skills, statistics knowledge, and machine learning techniques.

For new learners, it is often very difficult to figure out the right approach to learning and gaining the necessary hands-on experience to get opportunities in the field.

Apart from having a degree in mathematics/statistics or engineering, data scientists need to be technically proficient in programming skills, statistics knowledge, and machine learning techniques and also should have a good understanding of how business works.

Young learners always have a hard time figuring out how to start a career in the data science field, and what all must be included in learning in order to impress potential employers?

What’s missing from most of the data science programs available in the market?

The industry is overflowing with a number of Data science courses that provide academic proficiency but lack hands-on training and most do not provide interview preparation and job assistance.

Well! The answer could lie in a series of workshops by Zorba that dig deep into evaluating all the key features required by a data leader in order to establish a niche in this hottest growing industry.

The key features of Becoming a Data Leader series include:

• Business Acumen and Problem Solving
• Mathematics and Statistics
• Programming Aptitude
• Communication and Stakeholder Management

A bird’s eye view of each Micro-workshop series.

• Business Acumen and Problem Solving:

1. Identifying the core process and priority.

Massive data is generated, analyzed, and interpreted in all the businesses in this internet age. Therefore, it is vital for Data scientists to possess a keen understanding of the key elements and core processes of the business for effective interpretation of data.

2. Art of solving problems for a domain-

A vital characteristic that one needs to acquire in order to make a mark as a data scientist, is to have strong expertise in understanding business challenges and how data and technology can be optimally used to bring out effective business solutions.

3. Most complex vs Most implementable-

Companies need to understand their key business operations, the requirement of the customers and develop business models which are best suited considering what worked in the past, and what can be predicted for the future. What good will a complex business model serve if it is not the most implementable in solving strategic business solutions?

• Mathematics and Statistics.

1. Connecting numbers to business-

Mathematics is the backbone of any Machine Learning Algorithm and a necessary element of data science. A strong foundation in mathematics will give all data scientists a competitive edge over their peers in this cut-throat competition. By using numbers in business, we can create predictive models to find the best possible outcomes in order to improve performance. Some essential mathematical topics that we need to master in order to make a mark as a data scientist are –

Discrete Mathematics, Finite Mathematics, Real and Complex numbers, Rational numbers, Basic Geometry and Trigonometry theorems, Calculus, Linear algebra, Analysing social graphs, Probability, etc.

2. Stats for predicting the future-

Though data science and statistics share similar skills and common goals, they are both unique in themselves. Statistics uses established theories and relies mainly on hypothetical testing whereas data science relies heavily on computers and technology. But almost all data scientists would like to acknowledge that statistics is a very important discipline in applied machine learning and gives deeper insights for analyzing available data in order to predict the future.

3. Comparing numbers-

Very often in real-life business applications, we are required to analyze two data sets to find out their similarities and differences. For example, can you compare apples to oranges? Data analysts need to deal with such situations on a regular basis. While comparing variables in data science we look at relative metrics instead of absolute metrics. Correlation, normalization, coefficient of variation, standardization are some of the ways we use to compare different numerical variables for analyzing and building our predictive models.

4. Analytics in excel-

Excel is a very powerful tool in data analysis and is crucial in all businesses for making better decisions and day to day functioning. Excel functionality involves organizing various data sets, manipulating data for easier understanding. Popular excel features such as built-in-pivot tables are indeed one of the most important tools used in data analytics.

• Programming Aptitude.

1. Programming a necessary requisite to analyze businesses-

Though it is not mandatory for professionals to have a hardcore programming language science background beforehand it would be beneficial for a person who wants to establish himself in the data science field to have a clear understanding of programming concepts such as C, C++, R, Hadoop, SAS, Java, etc. Data scientists with skills in programming are generally preferred in this rising industry.

2. Understanding Jupyter notebook-

Jupyter Notebook is an open-source web application created by people at Project Jupyter to facilitate data scientists to combine live codes, equations, multimedia, text, and visualizations and share their results with people with non-technical background by converting them into formats such as Html, PDF, LaTeX, Executable script, Reveal.JS, Markdown, Restructured Text. Julia, Python, and R are some of the languages supported by Jupyter Notebooks. Jupyter notebook can be used by people with a non-programming background because of its ease of usability.

3. Twenty hours of python plan-

Python is undoubtedly one of the most lucrative languages in today’s scenario and data scientists with python skills can nearly fetch salaries above $1,00,000 in the US. It is one of the most commonly used languages and is very flexible and used in a number of industries. Learning the basics of python will not only enable professionals in climbing their ladder in their present jobs but also allow them to enter new ones. The twenty hours of python plan at Zorba consulting may be your answer.

• Communication and stakeholder management.

1.Understanding program management-

Large organizations have multiple projects with different teams running concurrently. Program management involves coordinating and collaborating with people and teams across different cross-enterprise projects under the same umbrella with an intention to achieve strategic goals. Data scientists need to inculcate program management skills in order to have a successful career, as these skills facilitate them to work with many stakeholders across multiple departments on a daily basis with an intention to improve the organization’s performance.

2.Three things to never mess with while dealing with leadership.

Data scientist’s job often requires them to work directly with people in leadership positions of the company. They need to have a deep understanding of the decision-making process and how it affects the working of the company.

Key points data scientists should ensure while working with the leaders are-

1. A data scientist should be crisp, clear, and concise while dealing with leaders. He should have a complete understanding of the feasibility of the projects and be mature enough to communicate the shortcomings and failures of the projects to the people in leadership positions in the most efficient and stable manner.
2. He should proactively communicate with the leaders and not hesitate in seeking help and intervention when required. He should make sure that he puts forth his opinions and suggestions in a non-offensive manner.
3. He should make sure that his goals are in alignment with the executive’s strategies and goals of the company.

3. Win-Win model of collaboration.

A successful business model involves collaborating with different teams in the company to find a win-win solution. Conflict resolution may be seen as an opportunity by the data scientists to analyze with the help of his equipped knowledge and find a middle ground that satisfies both the conflicting parties for the success of the company.

For example, the executives in the sales department of a bank would try and push as many credit cards as possible to meet targets whereas the risk department would hesitate to issue so many credit cards to avoid losses in case of defaulters. This is where a data scientist with the help of data analyzes the creditworthiness of the customers and finds a solution that benefits both the sales department and the risk department along with keeping the interests of the company in mind.

Apart from acquiring all these traits you also need to be creative and have a strong ability to remain focussed and pay attention to the minutest details to become a successful data scientist.

“Becoming a Data Leader micro-workshop series” by Zorba consulting could help you gather all the necessary skills with hands-on training to gain an edge over your competitors.