A data scientist is one of the topmost ranking professionals in any analytics organization. A data scientist is ranked on the topmost positions for the best jobs in the world. In today’s date, data scientists are scarce which is why they are highly in demand and because of that, there are excellent growth prospects in this field. The role of a data scientist is to understand the business problem, outline a data analyst strategy, gather and format the required information, apply techniques or algorithms employing the accurate tools, and then make suggestions backed up by data. For doing so, one needs to take professional training for understanding the subject and performing to the best of their capabilities.
For launching a career in data steps you need to perform certain steps such as:
1.Find out what you require to learn
Data science can be an overwhelming career as a lot of people will often tell you that you need to master certain concepts for stepping into this field, but that’s not exactly true. Data science is a procedure of asking exciting questions and then answering it employing data.
A data science workflow follows:
- Asking a question
- Collecting data for answering the question
- Cleaning the data
- Analyzing, exploring, and visualizing the data
- Evaluating and building a machine learning model
- Interacting results
The workflow doesn’t require advanced maths but a basic understanding of the subject along with programming language.
2.Getting comfortable with Python
Python and R are both great choices where R is more popular in academics and python in industry. You don’t require learning both but you should emphasize on understanding one language with its ecosystem of data science package. Most people prefer python today due to its benefits and user-friendly interface. If you are preferring Python then install Anaconda distribution, as it will simplify your process of package management, and installation on OSX, Windows and, Linux.
3.Understanding data manipulation, analyzation, and visualization with Pandas
For operating with data in Python, get acquainted with the Pandas library. Pandas are known to provide a high-performance data structure called data frames that are comfortable for tabular data with columns of a distinct type that is similar to a SQL table or an excel spreadsheet. Understanding pandas will increase your efficiency while working with data. Though pandas consist of an overwhelming amount of functionality and offer several ways to accomplish the task. These characteristics can make it quite challenging to learn pandas and figure out the best practices.
4.Learn Machine Learning with Scikit-learn
Building ML models for extracting insights from data or predicting the future is the best part of data science. SciKit is one of the most popular libraries for ML in python as:
- It provides a consistent and clean interface to several distinct models
- It provides many tuning parameters for every model and also selects sensible default
- The documentation of Scikit is exceptional and helps in knowing the models and ways to employ it properly
Though machine learning is still a rapid and highly complex evolving area has Scikit has a steep learning curve. For getting a grasp of Scikit for machine learning, one needs to go for proper training which will help in gaining an insight into Scikit-learn, equipping them better in the subject, workflow and machine learning fundamentals.
5.Learning machine learning in-depth
Machine learning is a complicated field, though Scikit offers the tools required for performing effective ML, it doesn’t answer certain questions directly like:
- How to know while machine learning model will work best with which dataset
- How to evaluate if the model will generalize to future data
- Ways to interpret the results of the model
- How to select which feature is suitable with the model
If you want to be comfortable and equip machine learning then you need to learn how to answer those questions and for that experience and further study both are required and for that data science training in Mumbai and data science training in Pune are the best options to go for.
6.Keep practicing and learning
One of the best pieces of advice for improving your data science skill is to discover the thing that motivates you to practice things that you have learned and perform better and then do that thing. That could be Kaggle competitions, personal data science projects, reading books, taking online courses, attending conferences or meetups, or any other thing.
- Granting to open source projects will help in practicing collaboration with others.
- Kaggle competitions are an excellent way to practice data science without coming with problems. You don’t need to be concerned about high your place but emphasize learning something new with each practice.
- Attend PyCon US if you are willing to have an actual experience with the Python community. There might also be other conferences but PyCon US is one of the best. You can also consider attending your nearest PyData and SciPy conference for assisting you better.
- Create your own data science projects and share it with others to get insights and views of how you can do better and also to inform them that you can do reproducible data science
Data scientists are professionals skilled in this field as they are well versed as to how to work on
huge amounts of data for tapping the areas facing difficulties and solve them with ease so that the company can grow. A rise in the demand for data scientists has led to huge growth in the profile of people working in this field. Data science is one of the most solid career choices for freshers as well as equipped professionals. These are some of the simple steps that you can go through for starting your career as a data scientist and walking through the path of success.