This post was released as part of the Data Science Blogathon.
When I started exploring this topic, I unraveled the mystery behind this magical phenomenon; it was none other than data science and machine learning. I found it quite surprising how the machines recommend similar products based on purchases made by different customers who could have bought similar posts together. E-commerce companies mainly focus on a recommendation system that allows them to suggest similar products to the user based on previous searches and purchases made by other users..
Even though I had decided to delve into the domain of data analysis and data science and change my career, still not sure how to do it. There were many courses that were available online at that time, which made the journey even more confusing for me. I subscribed to a plethora of materials and books online and six months later I still hadn't made any progress on the path to data science.
Like me, I'm sure there are many people who are excited to launch their career in data science. Even so, due to their work commitments, personal relationships and non-technical background, they have to resign if they do not find success in a year. Although this is an extremely common phenomenon, it does not have to be this way, if you want to become a data scientist no matter what it takes. I learned it the hard way, but lastly, discovered some great ways to jumpstart my data science journey. Then, let's start “.
Master a programming language:
- Learn the basics of Python: if you learn yourself, get started with learning the basics of Python. Get comfortable with coding in a particular IDE like Jupyter or Pycharm. They are both good in their own way.
- Learn and practice Python projects and solve problems using the concepts you have learned. You can start by building a project that analyzes your daily spending habit from platforms like Amazon, Big Basket, etc.
- Open Web Scraping with Python: it is absolutely essential to learn Web Scraping, as it helps you collect data and analyze it for your own benefit. I was working on a Canadian project where I had to scrape the details of electricians from the Toronto region, therefore I used web scraping to scrape the data from a site called kijiji.ca. It was very interesting to extract all the reliable data and later work according to the requirements of my company.
Learn statistics and data science algorithms:
You must be comfortable with the statistics, since statistics are implemented to solve business problems in daily life. You should also become familiar with data science algorithms., since they are useful. At the same time, solve any business problem or implement its use in any project based on data science.
You should also have a clearer understanding of the difference between classification problems., regression and grouping, since with this you can create a data science model separately. Depending on the type of problem you encounter, knowledge of these three machine learning techniques is extremely helpful.
Even if you don't like statistics, you have yet to learn statistics to advance your data science journey. I was never a fan of statistics, despite this, I found that without them I would not be able to understand advanced concepts. Statistical methods have been used primarily to ensure that the data collected by you has been correctly interpreted.. Principally, statistical analysis helps find the meaning of meaningless numbers in the data.
Over time, I started to enjoy learning statistics for data science. Here's what you need to learn for data science:
- Statistics and probability theory
- Probability distributions
- Hypothesis testing
- Statistical modeling and fitting
- Machine learning
- Regression analysis
- Bayesian thinking and modeling
There are many sources to learn Statistics from. I recommend learning the concepts of Udacity and Khan Academy. If you find it boring, Stats Quest Youtube channel is a fun way to learn statistics. If you are already enrolled in a course, religiously follow your curriculum for a better understanding.
Build a curriculum / structure to learn:
Learning without thinking may produce little or no results, since there is no external motivating factor to keep you going. If you plan to transition your career, if you are already familiar with data science and machine learning, be sure to plan your study in advance. It is essential to create a course plan and stick with it until you complete it.
If you plan to start your data science journey from scratch, you must enroll in a trusted course and follow its guidelines. Even though there are a plethora of courses out there, Companies like DataPeaker have launched an interesting line of courses that also give a guarantee of work if you follow their plan and program diligently.. It's a great way to stay motivated and complete your data science journey..
Stick to a particular plan and don't forget to review and learn new concepts daily.
Join communities and groups:
There are many free online groups in data science, where you can get many resources and online help. Once you are comfortable with coding and implementing concepts, don't forget to share your doubts and concerns if you get stuck. The experts will always be there to support you and solve your problems.
Start reviewing projects and rebuilding them
Reviewing existing projects and checking your code from start to finish can bring a whole new perspective to your learning pace.. Simple theoretical knowledge is not enough; putting the same projects into practice live can accelerate your career very fast. To better understand, you can always start a project with a good amount of knowledge. As an example, I worked in the financial sector, so I decided to start with a trade hurdle that related to my area of expertise. With my knowledge of the domain and my skills in data science, I was able to understand the constant concern the company faced. With my data science skills, knew exactly which model implementation could produce results.
Good, this is how i started my journey. I am very happy with the progress I made, and I have seen my peers who have worked towards their passion for data science and have successfully turned their career into data science.
About me:
Hello there, I am Ananya and I am a passionate blogger and business analyst. My data science journey has just begun and I'm enjoying every part. I can work on two of my best domains: data science and e-commerce, and i can't be prouder of it.
The media shown in this post is not the property of DataPeaker and is used at the author's discretion.
