Skip to main content

My Data Science Journey and Suggestions - Part 3

If you haven't read the Part 1 and Part 2 of my Data Science journey, I recommend you to read it before you go through this article. Link to the Part 1 and Part 2.

In the sixth semester, I worked on multiple Deep Learning projects. Neural Artistic Style Transfer was one of my favourite projects I have worked on. I have used TensorFlow in this project and this was my fist Computer Vision project. I enjoyed doing it. I felt that I had a decent number of projects on my profile. So, I started applying to Data Science internships through LinkedIn. I requested my connections on LinkedIn to refer me in their companies. I received calls from a few companies. I gave interviews and ended up with two offers, one from a startup and another from a well established company. After doing research and reviews on glassdoor, I accepted the Data Science Summer Internship offer from Monsanto. It was a unique experience giving interviews from various companies.

I am fortunate to get an internship at a very good company which helped me to boost my career in the field of Data Science. My mentors helped me in both personal and technical development. This internship was a life changer for me. During my internship, I was exposed to half-day hackathons. Hackathons are really helpful to learn things in very less time. They encouraged me to participate in hackathons, write posts on LinkedIn. I followed their inputs and the results were amazing.

In the seventh semester, after the internship, I continued participating in Data Science hackathons on HackerEarth. I started sharing my knowledge and achievements through LinkedIn posts. The Data Science community on LinkedIn was very huge. I started Kaggle in the month of October 2018. I learnt a lot of visualization and preprocessing techniques in just three months. I became "Kernels Expert" after continuous effort. I successfully completed Statistics and Data Mining courses that were provided by the institution. These courses helped me to get an intuition of different machine learning algorithms deeper.

I have also worked on Time Series analysis on Maharashtra Rainfall data, which was a research project. We bought the rainfall data from the IMD website for this project. The real data science starts only when we work on real data. During this project, I read Time Series Analysis book, written by Box Jenkins. This was really a good book. If you have time, I suggest you read this. I have created multiple kernels on Kaggle on time-series data. If you are a beginner in time series analysis, do explore my kernels. I have used both traditional and deep learning methods to solve the problem.

By this time, I had a decent Data Science profile. So, I started applying to jobs through LinkedIn. I was looking for a Data Science job in MNC. I applied to 50+ companies and got a response from 5 companies. I gave interviews to all the companies. The interview process was completely different from company to company. But, there were at least 3 rounds. I was ended up with 3 Data Science Associate offers. For one of the company, the interview process had 6 rounds and I was able to crack all of them, but due to some reasons, they delayed the offer. In the end, I happily accepted the offer from ZS Associates for the role of Data Science Associate.

The struggle was real. We don’t get a response from every company that we apply to. We need a lot of patience and trust in ourselves. It took me two years to explore different domains of Data Science and struggle for six months to get a job offer. Now, I am proud of myself.

Final Suggestion:

The competition is very high. Sometimes, our resume may not reach to HR, due to the number of resumes they receive every day. So, we need to stand out of the crowd. I suggest everyone spend time on improving their skills and profile. Data Science is an ocean. We cannot master it in a few days. It will take time and consistency.

Hope, my journey will help you to make good decisions in your Data Science career. If you have any queries, comment in the comments section below. I would be more than happy to answer your queries.

Thank you for reading my blog and supporting me. Stay tuned for my next article. If you want to receive email updates, don’t forget to subscribe to my blog. Keep learning and sharing!!

Follow me here:
If you are looking for any specific blog, please do comment in the comment section below.


  1. I read your all of the three posts, it taught me a lot. Thanks for writing. I want to get a job as a data analyst, i have started learning about data science from 7th semester, can I get a job at the end of my science)?? Is it possible in 1 year , if yes, then how ??

    1. Thank you for reading all my articles. Yes, it is possible. I wrote an article on skills required to become a data scientist.

      Here is the link:

      Make sure that you re comfortable with all the skills that are mentioned in the blog. And then start applying to jobs. I highly suggest you participate in the Data Science hackathons.

  2. I am glad to hear the news, that you managed to get a job in a company. Not everybody can have such a good opportunity. I wish you good luck.

  3. Very inspiring journey, Thats great Abhishek.

  4. Hi Abhishek , Your journey is really inspiring.
    I too started my journey from course of coursera , as a part of job work I also happened to work on Arbitrary Neural style transfer .
    I am also new to this field and from ECE , do u suggest having a good knowledge on web development and software product development and related skills are very much necessary ?


Post a Comment

Popular posts from this blog

Google Colab - Increase RAM upto 25GB

Google colab is a free jupyter notebook that is hosted on Google cloud servers. We can use CPU, GPU and TPU for free. It helps you to write and execute your code. You can directly access this through your browser. If you want to use Google Cloud/AWS, it requires hell lot of setup. You have to spin a cluster, create a notebook and then use it. But, Google colab will be readily available for you to use it. You can also install libraries from the notebook itself. These notebooks are very useful for training large models and processing huge datasets. Students and developers can make use of this because it’s very difficult for them to afford GPUs and TPUs. I was trying to run a memory heavy job. The notebook crashed. Then, I came to know how I can increase the RAM. So, I thought of sharing it in my blog. There are some constraints with the notebook. You can run these notebooks for not more than 12 hours and you can use only 12 GB RAM. There is no direct method or button t

Skills required to become a Data Scientist

Data Science is one of the hottest areas in the 21st century. We can solve many complex problems using a huge amount of information. The way electricity has changed the world, information helps us to make our lives easier and comfortable. Every second, an enormous amount of data is being generated. The data may be in the form of text, image, speech or tabular. As there is a lot of growth in the field of Data Science, in recent years, most of the companies have started building their own Data Science teams to get benefited from the information they have. This has created a lot of opportunities and demand for Data Science in different domains. For the next 5+ years, this demand would continue to increase. If we have the right skills, companies are ready to offer salaries more than the market standards. So, this is the right time to explore and gain skills which enables you to enter into this field. We have discussed the importance and demand for data science in the market. Let’s disc

Top 35 frequently asked Data Science interview questions

Interviews are very stressful. We should prepare for the worse. So, we have to plan accordingly in order to crack them. In this blog, you will get to know the type of questions that will be asked during the interview. It also depends on the experience level and the company too. This blog is mainly focused on entry-level Data Science related jobs. If you haven’t read my previous blog-posts, I highly recommend you to go through them: Skills required to become a Data Scientist How to apply for a Data Science job? First of all, you must be thorough with your resume, mainly your Internship experience and academic projects. You will have at least one project discussion round. Take mock interviews and improve your technical and presentation skills, which will surely help in the interviews. Based on my experience, I have curated the topmost 35 frequently asked Data Science questions during the interviews. Explain the Naive Bayes classifier? In case of Regression, how do y

My Data Science Journey and Suggestions - Part 1

I always wanted to share my detailed Data Science journey. So, I have divided the whole journey from BTech first year to final year into 3 parts. I will share everything, without leaving a single detail, starting from my projects, internships to getting a job. You can follow the path that I have followed if you like my journey or create your own path. In 2015, I got a seat in Electronics and Communication Engineering (ECE), IIIT Sri City through IIT JEE Mains. Because of my rank in JEE Mains, I couldn’t get into the Computer Science department. I wanted to shift to Computer Science after my first year, but couldn’t due to some reasons. In our college, we have only two branches, CSE and ECE. For the first three semesters, the syllabus was the same for both the departments except for a few courses. This helped me to explore Computer Science. In the first 3 semesters, I took Computer Programming, Data Structures, Algorithms, Computer Organization, Operation Systems courses, wh

Exploratory Data Analysis and Data Preprocessing steps

Exploratory Data Analysis is the foremost step while solving a Data Science problem. EDA helps us to solve 70% of the problem. We should understand the importance of exploring the data. In general, Data Scientists spend most of their time exploring and preprocessing the data. EDA is the key to building high-performance models. In this article, I will tell you the importance of EDA and preprocessing steps you can do before you dive into modeling. I have divided the article into two parts: Exploratory Data Analysis Data Preprocessing Steps Exploratory Data Analysis Exploratory Data Analysis(EDA) is an art. It’s all about understanding and extracting insights from the data. When you solve a problem using Data Science, it is very important to have domain knowledge. This helps us to get the insights better according to the business problem. We can find the magic features from the data, which boost the performance. We can do the following with EDA. Get comfortable with

SHAP - An approach to explain the output of any ML model (with Python code)

Can we explain the output of complex tree models? We use different algorithms to improve the performance of the model. If you input a new test datapoint into the model, it will produce an output. Did you ever explore which features are causing to produce the output? We can extract the overall feature importance from the model, but can we get which features are responsible for the output? If we use a decision tree, we can at least explain the output by plotting the tree structure. But, it’s not easy to explain the output for advanced tree-based algorithms like XGBoost, LightGBM, CatBoost or other scikit-learn models. To explain the output for the above algorithms, researches have come up with an approach called SHAP. SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate ad

Complete Data Science Pipeline

Data Science is not just modelling. To extract value out from Data Science, it needs to be integrated with business and deploy the product to make it available for the users. To build a Data Science product, it needs to go through several steps. In this article, I will discuss the complete Data Science pipeline. Steps involved in building a Data Science product: Understanding the Business problem Data Collection Data Cleaning Exploratory Data Analysis Modelling Deployment Let us discuss each step in detail. Understanding the business problem: We use Data Science to solve a problem. Without understanding the problem, we can’t apply data science and solve it. Understanding the business is very important in building a data science product. The model which we build completely depends on the problem we are solving. If the requirement is different, we need to adjust our algorithm such that it solves the problem. For example, if we are build

How to apply for a Data Science job?

Job search is one of the painful tasks. We have to invest a lot of time to get placed in one of the best companies, we were dreaming for. The demand for Data Scientists is increasing over the years, and we have to stand out of the crowd to get a job. In this post, I will guide you on “How to apply for a Data Science job?”. I have divided the blog post into the following: What are the skills required for a Data Science job? How to build a good Data Science profile/resume? What are the different ways of applying for a Data Science job? What are the skills required for a Data Science job? I have created a blog-post on “Skills required to become a Data Scientist”, last week. I would suggest going through the previous blog before you go to the next section. How to build a good Data Science profile/resume? After acquiring the necessary skills, it is required to maintain a good Data Science profile. Your presence on the social network makes a difference too. Some tips

Building ML Pipelines using Scikit Learn and Hyper Parameter Tuning

Data Scientists often build Machine learning pipelines which involves preprocessing (imputing null values, feature transformation, creating new features), modeling, hyper parameter tuning. There are many transformations that need to be done before modeling in a particular order. Scikit learn provides us with the Pipeline class to perform those transformations in one go. Pipeline serves multiple purposes here (from documentation ): Convenience and encapsulation : You only have to call fit and predict once on your data to fit a whole sequence of estimators. Joint parameter selection : You can grid search over parameters of all estimators in the pipeline at once (hyper-parameter tuning/optimization). Safety : Pipelines help avoid leaking statistics from your test data into the trained model in cross-validation, by ensuring that the same samples are used to train the transformers and predictors. In this article, I will show you How to build a complete pi

A year of experience as a Data Scientist

On June 3rd 2019, I joined ZS Associates as a Data Scientist after graduating from IIIT SriCity. It was my first job and was very happy to get placed as a Data Scientist through lateral hiring. If you haven’t read my Data Science journey, please read it here :) After joining, I had some awesome moments that I never experienced since childhood. I got a chance to stay in a 4 star or 5 star hotel multiple times. I got a chance to travel by flight. I travelled to Pune, Delhi and Bangalore. I saw Vizag, Pune, Delhi and Bangalore airports in less than six months. I loved it. A few office parties, outings during Diwali and New year celebrations. Above are some of the moments that I can never forget in my life. My first job allowed me to experience these first time moments. Enjoying life is more important than anything. If you don’t enjoy your life, you cannot achieve anything big. Okay, let’s go into the main topic in detail. Me (inner voice during BTech):