Taking a step forward to becoming a Data Scientist — Getting hold of technical aspects
With new errands and industries accumulating varied aspects of data into their daily functioning, the scope of magic that data science can drive to their profits is immeasurable. Along with scary terms like data warehousing, deep learning, and neural networks, upcoming enthusiasts or job seekers must gain the industry-desired skills to cope with the enormous demands of the recruiters. After the recession attacking the survival of several domains, the data industry kept rising to peaks. Despite the expensive nature of the industry, it still flourishes!
It has data at its core for the most obvious reasons, which keeps enhancing multi-folds each day. However, today, we aren’t here to discuss the progress in the industry. For certain reasons, if you are reading this, you already know that! You are here to kick-start your journey.
Hence, let’s right away focus on the basic pre-requisites of becoming the best data scientist.
- Statistics
- Python
- Machine Learning
- Data Mining
STATISTICS:
Starting off with the first one, statistics! Statistics is the core behind Data Science. Usually, the aspirants of data science jobs fail to understand the need for statistics! They tend to overlook the secrets and the mystery that statistics can unfold for them. Here is an overview of important statistics topics.
Statistics give you the power to answer the questions that arise from data. It helps you to understand what the data hold which can not be looked at through a mere glance at data. You need to understand a few concepts in statistics before you initiate your next steps towards data science. The most important topics are those which you usually are scared of! After all, the sexiest job of the century doesn’t come that easy, it is demanding, just like your crush-unachievable yet desirable.
Focus on-
Probability, Permutation and Combination
Descriptive Statistics
Distributions and Estimations
Sampling Techniques
Hypothesis Testing
Correlation and Regression
PYTHON:
Once, you are thorough with your statistical understanding of your data, you might wish to focus on getting hands-on a programming language without which there is absolutely nothing possible. After all, you desire a job in the coding industry. Why learn Python? What is it going to help me with?
Python is the highest trending language in the 21st century and as of now provides you the ease to do anything you wish to. Nevertheless, it is easy to learn, easy to understand, and you can find the cheat tricks everywhere on the internet very easily.
However, basic python is crucial to your coding in data science as you can not run after small errors and keep on consulting StackOverflow every now and then. You know what I mean! ;) This is going to cost you a lot of time. Hence, basic functions and concepts in python are a necessity. At least you should be comfortable with the syntax, the least.
MACHINE LEARNING:
Moving on to the modeling part of data science which makes it all sexy and hot, the Machine Learning. Usually, Machine Learning is a part wherein your data is fed to a model, and outputs are generated based on that.
Machine Learning as the definitions imply is making the machines learn by training them. The important aspect is you learn how to generate several models and the idea behind using the models for specific purposes. You should have a clear understanding of the model in terms of — when to use a particular model, what kind of data can be fed to it, what are the probable output types it can deliver, what are its hyperparameters, and how can you improve its efficiency. Well, in order to master the model building process in ML, you must familiarize yourself with the libraries that will save you from all the troubles of coding from scratch.
Get hold of :
Matplotlib.pyplot
ScikitLearn
Pandas & Numpy
Tensorflow & Keras
OpenCV
These are a few essential libraries which you will use for your data science, however, it is a good idea to get hold of visualization libraries like Bokeh, it just makes your work easier.
The best way to get hold of these libraries is ‘go to their official documentation and start implementing.’
DATA MINING:
Well, this involves a lot of scratching your head and worrying about your stresses. Basically, this is the heart of data science, and the blood is data. So, data science basically stops living if you skip this.
Data mining is a process of deducing and identifying patterns in enormous data sets involving processes at the junction of machine learning, statistics, and database rules. Now, you may already be like, dude, you said data mining, why does it sound like SQL? You may wish to get a hold of SQL to identify several patterns in the data. However, Python is capable of doing it, just that you need to smartly put up right features in place.
CONCLUSION
Data science is definitely one of the highest paying jobs and the money is the reason it keeps attracting so many people from varied fields. However, what goes into becoming a successful data scientist is not a cakewalk that anyone can traverse. You need to be consistent and practical. Have fun looking at data and comprehending what it could say to you, and you will be in the field jumping to the top.
