Data Science is a vast field where statistics and programming go hand-in-hand. In order to ace this field, enthusiasts must follow a learning routine that involves practising, reading, competing as well as engaging with the community. This is a 4-weeks plan which can be repeated every month to enhance your depth of understanding in Data Science. It includes both theoretical and real-world practical resources related to data science and machine learning. The plan is tailored to provide you with the necessary tools one need to become a master in Data Science.
In this article, we discuss 4 simple yet powerful weekly self-study methods, which will help a data science enthusiast to be ahead of the curve.
At A Glance
Program Type: Self-Paced
Estimated Duration: 4 Weeks
Pre-Requisite: Basics of Machine Learning, Big Data, Python, SQL
Tools: Python, R, SQL, Hadoop, MapReduce and Tableau
Note: This program is repeatable, and one can repeat this every month to understand more advanced topics and new projects. To start this weekly program, one must have to have the basic knowledge of machine learning and its techniques.
Week 1: Brushing Up The Theory
Considering you already know the basics of machine learning, it’s time to move beyond the basics. In this part, you will need to decide the subcategory of data science such as image processing, text processing, time-series analysis, among others where you wish to proceed by the hands-on project. Besides this, one more important topic that you need to understand is big data as you will be going to play with a huge amount of data in the long run. This week you can take up any curated courses from MOOCs and other course works, which are related to these categories and learn the theory behind.
Massive Open Online Courses (MOOCs) not only provide a wealth of information, which helps aspirants by offering a complete and comprehensive content of some specific topic but also evaluates through assignments that assist in brushing up the knowledge. You should enrol in some popular part-time course in data science, which are available online to grab an in-depth knowledge of various topics.
Week 2: Play With Data
The week will be followed by working with lots of data where you will need hands-on knowledge in languages like Python, R and SQL. For the visualisation of data, you can use popular tools like Tableau, Power BI, etc. We have mentioned Tableau as currently, it is the most popular one among organisations.
At the present era, data is one of the crucial features of any organisation as it helps in gaining meaningful insights and decision-making. Data is growing every day, and with this, organisations are in need of such experts who have a solid understanding of how to extract knowledge from these data. In order to work with big data, one must use tools such as Hadoop, MapReduce, Scala, etc.
There are several popular datasets available online, which one can use to work on data science projects. For instance, there is Kaggle, UCI machine learning dataset, etc. where you can find datasets to work on methods like data preprocessing, data munging, data wrangling, data cleaning, etc. Click here to view 10 datasets, which can be used for data cleaning practice or data preprocessing.
Week 3: Explore Real-World Project
Now that you know the theory behind getting your hands dirty with methods like data preprocessing, data wrangling, etc., it’s time to work on some real-world project. For instance, if you have chosen subcategory image processing, you can work on projects like facial recognition, object detection, among others.
Practising real-world projects is one of the most crucial parts that one must follow on a daily basis. This will enhance practical knowledge as well as provide in-depth knowledge of the domain. These projects will provide a clear view on how to build complex machine learning systems, how to visualise the data and gain insights, which models fit best into which domain and much more.
Week 4: Time To Showcase Your Skills
This is the last and final week where you will need to gather all the knowledge of these three weeks and put them into one. GitHub is the perfect place to showcase your problem-solving abilities in coding and other software capabilities in a way that does justice to your skills. Fork repositories, work on a variety of open-source projects, solve complex ML problems, etc. on GitHub.
While applying for Senior Data Scientist profile, the recruiters will pay attention in the areas such as the variety of projects that you have brought, completeness of the project, how many times it got forked, its functionality, readability and most importantly the information that it stores and displays. This will certainly help you to gain an edge over other competitors in the field of data science.
It is important to create a learning routine while setting a definite path to achieve the milestone. This weekly self-study plan is perfect for those who want to understand data science from its core and have the curiosity to stay ahead of the game. Besides this, you can also attend tech meetups or conferences and read the latest research papers during weekends, which are related to machine learning and data science.