Demand for Data Science and Data Analytical skills are growing exponentially day by day as there is a large scale shortage of deserving candidates in the global market. The expected growth of data generation in 2020 is 50 times more than that is in 2011 and by taking this thought in consideration, we cannot deny the fact that we need a number of best conjurers in data science who can handle large bunch of data sets and create unexpected results to drive business growth and innovative ideas. It makes the data science a sexiest, career promising and lucrative job field for anyone looking to start their career as a data scientist or data analyst.
Data Science is a step towards of business analysis that consists of applying techniques and practices of computer data science, logical mathematics, computer statistics & analytics and modelling of datasets to drive business growth. It involves the process of leveraging automated methods to study & analysis of huge amount of data in order to fetch out useful insights from it, showing from where array of information coming in and what it represents & how it can be turned into useful set of information in creating right business strategies.
In this guiding article, we have tried our best to show you almost every data science learning steps-tips which will be beneficial to understand the most efficient way of becoming data scientist or data analyst.
- Develop Skills in Mathematics(Linear Algebra, Probability, Statistics):
Let’s take example of Drone Company that works on to track crowd surveillance and want to find out the number of male and female candidates at some specific place. And now for this task from long distance, you need a strong hold on probability and statistics. Probability will help you out to track number of presence of male and female persons on the basis of their face attributes and physical appearance. Mathematics is an important task for any data scientist because handling of data and creating best data enabled products actually requires an ability to view data and its patterns and textures proficient mathematical skills and mindset. Once it converted into easy to understandable structured form of data, then it’s time to analyze and visualize this set of data which definitely requires must have excellent knowledge of statistics. It is also very important to talk about the term matrix if you want to reveal some hidden characteristics of datasets reflecting users’ behaviour in data science. If you are from a non-technical field, learn applied mathematics and develop a solid understanding of statistics before you dig your hands on Data Science. If you are already an analyst or statistician, just brush up your existing skills.
- Try To Gain Knowledge of Programming Langauge:
It doesn’t matter in which company or organization you are working in but as a data scientist or data analyst, you are highly expected to have good knowledge of any programming language. For working on complex data sets, a data scientist must know how to code which helps in cleaning and organizing unstructured form of data. The most prominent programming languages & technologies that require excellent hold on are Python, R. SAS, SPSS, Perl, SQL & NoSQL.
- Put Efforts To Learn Machine Learning (ML) Algorithm:
Machine learning is a crucial part of data science. Machine learning refers to the vast collection of methods which deals with data modelling process. Machine learning process is used to train computers and similar smart devices to learn and develop continuously by feeding them with new sets of data. It is used to make predictions from number of data sets by using different algorithms. Self driving cab companies, recommendation engines, recruitment companies etc presently are heavily using machine learning to upgrade their business ethics and to improve user experience. It is mandatory for any data scientist having familiarity with machine learning tools-techniques like K-Nearest Neighbours (KNN), Random Forests, Linear Regression/Logical Regression, Decision Trees, K-Means, Naive Bayes, Dimensionality Reduction Techniques, Support Vector Machines, Gradient Boosting Machines etc. It helps companies to automate their important tasks in real time by reducing the cost of operations and human interventions. A good scientist with great knowledge of machine learning can able to create robust-reliable systems helping to make high value predictions & take real time decisions.
- Grasp The Knowledge of Databases:
In data science domain, knowledge of relational databases is very important for any data expert that helps to access, manipulate and store large scale of valuable data all the time. MySQL and NoSQL databases such as MangoDB and Cassandra is mostly used by all data professionals to carry out data science tasks effectively. Solid knowledge of databases by data scientists is a necessity to shine their career as data professional.
- Learn Big Data Technologies:
Big data is basically a huge amount of data generating from different number of sources at higher velocity which cannot be easy to handle with old traditional databases but big data technologies are better solution for this. Knowledge of big data technologies like Hadoop, Spark, Scala, Hive, Pig & Map Reduce is the game changer opportunity for data science professionals. Most of the data scientists frequently work with large data sets that cannot be run on a single machine and require distributed data processing. Hadoop and Spark are open-source software technologies used for distributed storage and processing of datasets of big data.
- Get Your Hands Dirty in Data Wrangling Data Reporting & Data Visualization:
Data wrangling is the process of transforming raw data into useful data, i.e. cleaning up huge messy data sets into a convenient form prior to data analysis as collection of data from business is often not in good readable form and difficult to work with. So is the reason behind data wrangling process that data scientists repeatedly clean the data in their records before they use it to draw visionary insights.
Whereas, data visualization process is the creation and study of visual representation of the insightful data based on data wrangling process by using statistical graphs, plots, bar graphs, pie charts, graphical information etc.
Data reporting is the process of managing data into informational reports with core objective to gain meaningful insights for improving & monitoring various working domains within a business infrastructure.
Once you learn the above mentioned skills, now it’s a time to apply these skills and get your hands dirty by doing some real life projects which can be better option to start over with site Kaggle that not only give you the opportunity to take on complex problems but also give real set of data dumps to solve them.
If you have successfully completed few number of projects, passed multiple online tests and very well aware of data science concepts then start searching for job opportunities as data analyst from startup companies to big companies like Amazon, Google, Microsoft etc. But if you are already working in some other organization and want to switch to data science then no need to worry as demand for proficient data analysts and data scientists is increasing day by day.
Conclusion:
Working as a data science expert is a great option for anyone that is much rewarding and interesting which clearly point out that data scientist profession is going to explode in next multiple decades. It is a very challenging role to perform with long term success to build strong foundation in this domain.
Best of Luck! To all interested and aspiring candidates.
