With a growth rate of 29% every year, Data Scientist has been a dream job profile for every person having a background in statistics and analytics. The word on the market is Data Science is the sexiest job of 21st century, and why wouldn't it be? After all, data is the new oil and we are just beginning to comprehend the wonders data can do.
But yes, becoming a data scientist is easier said than done. You gotta possess a variety of skills to land up a role as a data scientist in any reputed company. Hence, before aiming for a career in data scientist, make sure you do have acquired such skills.
1) Dealing with complex datasets:
You need to have a strong liking for datasets and an unstoppable urge to clean them accurately before analysis. Every data tells a story and you need to be a good storyteller. Any sort of unstructured data needs to be organised using certain tricks and techniques, something which is basically the first step a data scientist should do. An unclean data will create hurdles during analysis, which in turn give rise to inaccurate results and false predictions. Hence, if seeing any error bound dataset awakens the OCD in you, you are good to go!
2) Efficiency in Programming:
You don't need to be an advanced, hardcore programmer to work and manipulate data, or to build models. But a basic understanding of programming is a must, especially loops, conditions, databases, etc.
Python and R are the prominent programming languages in Data Science, followed by SAS. Python has a fast processing rate and is versatile, which makes it a go to language for all data scientists. It helps the users organise large datasets in a short period of time. R has a huge amount of libraries made specially for statistical computations. It also has effective visualizations making your presentation of the results much easier.
Make sure your fingers don't hold back while typing those programming codes while building models for better prediction.
3) Strong Statistical Background:
The entire field of Data Science is balanced by a strongly cemented pillar of Statistics. It is one of the major subsets of Data Science. From understanding of basic statistical terms like Mean, Median, Mode, Variance, Standard Deviation, quartiles, etc., to understanding of a variety of distributions, hypothesis testing and Anova tables.
You don't need to become the next CR Rao in statistics, but knowing your way around statistics and its terms can help you derive a good summary of the dataset you are working on.
4) Knowledge of Machine Learning and Artificial Intelligence:
AI is the future, and it is impossible to separate AI from data science. Machine learning is growing at an exponential rate and it is a great tool to enable the machines to derive insights from past data. One must be able to develop algorithms which collects data and predicts data using appropriate statistical models, and is flexible in accordance with the new input data. The basic approach in machine learning is to scan the data and identify patterns which is followed by modifying the program as per user's choice. You need to have a strong grasp of ML techniques such as supervised Machine Learning, Decision Trees, Logistic Regression, etc. Having knowledge of these techniques will make solving of Data related problems much easier. Hence, if you wish to stand out as a data scientist, then having AI and ML knowledge will add another feather to your cap.
5) Ability to create effective visualizations:
Let's be honest, not everyone will be able to comprehend data at the same pace as you. Yes, you did all the data cleaning, data analysis, data modeling and derived your own predictions, but how will you explain your findings to someone who doesn't isn't that friendly with data as much as you? As a result, it becomes mandatory to translate your data in a common format that will be easy to understand to the common public. Data Visualisation Tools such as ggplot2, Matplotlib, Tableau, etc will help you translate your complex findings into effective charts and aesthetic graphs that would be beneficial for the stakeholders involved in the project. Data Visualisation and presentation is rightly the finish line in your journey with that dataset. Therefore, you not only need to be good at problem solving, you need to be good in presenting those solutions too. "A picture is worth a thousand words.", that was one of the first proverbs we learnt, didn't we?
Conclusion:
It is indeed a good decision when you aspire to become a data scientist. With so many big tech companies recognizing the role and value of data scientists, it really is the best time to polish your skills and excel in your data science journey. The best part is that, you could learn all of these skills online for free. Hence, quit overthinking and take the first step in becoming an expert data scientist.
You will definitely encounter a lot of difficulties and hurdles in this journey, but hey, we are data scientists, solving problems is what we do.
Comentarios