#critical_problem_solving_skills

Data Science has become a backbone for many industrial organizations today.  Many students are pursuing a career in Data Analytics and Data Science to hone their skills, to take advantage of the numerous opportunities existing in the markets..   With Data Science,  we use the raw data set to gain insight, or understand the trend or projections to enable management to make data-driven decisions using various statistical, mathematical and computational models and tools.  Therefore, to make a career in Data Science you will need a basic mathematical and Statistical foundation, computing skills, critical problem solving skills, strong analytical and presentation skills, and creativity.  The role of a Data Scientist could range from saving operating cost of the company, to mining data to provide valuable insight for better decision making, to improving customer experience while using websites or app etc

Python is one of the most versatile languages out there today capable of building machine learning applications with the ability to make predictions with the data mined.  For Data Analytics, we have two main libraries, which are the workhorses  of python called Pandas and Numpy, which can mimic many built-in-features of R Language, and we have Matplotlib provided for creating Visualization and SciKit-learn for machine learning with in-built algorithms and models for classification, regression, clustering, dimensionality reduction, model selection,  pre-processing.  For statistical inference models you have statsmodels.  For Deep Learning you can choose Keras, TensorFlow or PyTorch. On the other hand,  R language also have some useful packages for Data Science  like ‘tidyverse’ which is a collection of useful packages like dplyr for data manipulation, tidyr for data cleaning, and packages like purr and tibble for built-in-functionalities of R.    Data is driving major industrial sectors today, and the demand for data scientists are only growing day by day.  You gain skills by doing more and more projects.  So be patient and focus on the task in hand. 

A good Data Science program ensures that the student receives the right amount of practical and theoretical knowledge and skills to face real-world challenges.  We at eduJournal (www.edujournal.com),  make Data Science professionals industry-ready by providing them training and industry-driven projects by catering to the needs of industries.  If you are interested, please do contact eduJournal for more details.

The following are considered essential, if you intend to make a career in data science.

1. Develop a basic knowledge and understanding in Statistical, Mathematical and Computing concepts: Data Scientists are professional who turn data into insights or tends or help making predictions and recommendations. Having the knowledge of various concepts, tools, libraries and mathematical models are central to applying them.

2. Develop communication and presentation skills: Your analysis need to be communicated to the management. A good data scientist can contextualize and interpret the solutions to the stakeholders of varying backgrounds through various forms of communication viz., written communication (ie., in the form of report and Summary), visual communication (ie., clear and intuitive plots, analytics and visualization), and spoken communication (ie., presentation, iterative design, project specifications etc).

3. Identify promising opportunities that align with your skill and intellectual ability: We all have individual skills and intellectual ability, which we would love to develop and nourish. Make an assessment of how well your aspirations and goals align with the critical project path of the company or the environment you are in.  Select the projects of companies whose critical path best aligns with your skill and ability. For eg., if you have a strong data-driven skills for developing models using machine learning, then join a team or project that best aligns with your skills that enables you to understanding the workflow of machine learning, building complex pipelines with python and solving concrete business problems, which will ultimately enable you to develop your career in machine learning.  There is no point in applying for a data scientist’s job that focuses on experimental design and product analytics when your interest is somewhere else. Do not join a job just because it is popular, lucrative and in-demand.  You need to take into account your skill, knowledge and ability to perform in that job well.  Also, try to gain practical exposure through internships.  Keep yourself updated with the latest trends in industry viz global activities is your domain, technological advancements, best practices etc

4. Upskill/Reskill yourself with programming languages, libraries, and tools: A very common questions among Data Scientists on which programming language to choose. “Instead of thinking about which programming language to learn, think about which language offers you the right set of Domain Specific Languages(DSL) that fit your problem. – Reditt”.  In other words, it’s not about whether I should learn Python, but it’s whether Python provides me with the right library and tools to get my job done. Let’s take another instance of R Language and Python.  If you are developing statistical models then R is the ideal choice. Research scholars develop statistical method for developing newer R packages.  On the other hand, Python is best for building production data pipelines for machine learning. Instead of depending on a specific library or tools, or a program language, identify the best set of resources that will enable you to solve a particular problem. There are other languages as well to support Data Science like Matlab and Julia but the most prominent are Python and R languages

5. Learn from experts before you take a leap: Choose a training program such as Master Program for Data Analytics and Data Science from eduJournal , that strengthens your skills and gives you hands-on-experience while you work on projects and deal with continuous practice, case studies and assessments.  You will get an industry recognized certificate after completion of the course to validate your skill in Data Science, along with assured placement assistance, and  insightful sessions with industry experts. Also, we would assist you to build a digitally professional portfolio that can be easily shared across with recruiters showing your proficiency in Data Science.

Getting placed in a Data Science sector will never come easily.  You need to put in enormous hardwork and attend numerous interviews to be successful.   The key to becoming a successful Data Scientist is to gain the required knowledge and skills in Data Science, and the best way to achieve it is by working on some real-world  projects.  Keep in mind the points mentioned above before placing your feet into your new job. For a career in data science, it may serve you well if you are able to represent data science on a venn diagram as a confluence of statistics, programming and domain knowledge.  Despite each occupying a proportionate share in the intersecting area, some may warrant a higher weightage than the others. 

Data Science has a curious distinction of being one of the few fields of study that leaves a practitioner without a domain. For instance, we have demand for professionals who can build financial analytics programs to foresee two main objectives viz., to predict profit and the other to protect the bank assets.  Data Science has the innate ability to mine large data (data mining) and uncover profitable insights through pattern recognition which is an invaluable skill. Also, Data Science can help banks minimize credit risk by uncovering risky behavioral patterns of individual owners and institutional borrowers.

Opting for a career in Data Science is a lucrative and in demand job across multiple domains like healthcare, banking, retail, education, government, transportation, media and communication etc.,   Therefore, taking sufficient time to understand the core concepts will not only help you during the interview process, but will also help you to understand whether you are truly interested in the Data Science career.