It does not matter how much experience you have, actually anybody can start or switch to data science and machine learning. The only important this is, how much eager you are for it. What it means to you. If you are very much keen to work in this field then nobody can stop you. There might be some short term hurdles however if you are focused enough and know your goals regarding where you want to see yourself after certain years, then you will definitely be successful in overcoming those hurdles.
If you are an aspiring data scientist or an experienced professional then E-network is here to help you to quick-start your Data Science and Machine Learning journey without any charges. Their service includes a comprehensive consult to help you with interactive interview sessions and comprehensive assessment report that includes detailed skill analysis, and suggestions. E-network also offer a quality mentor-ship that will help you get there quickly and smoothly.
For me no matter if you are experience professional or a fresher who want to start his career in Data science and Machine Learning, I can say starting point would be the same. As both have one thing in common. They both don’t have any prior knowledge of data science or supportive technologies which will be used in this field.
In case you have some prior knowledge on any of the technologies, topics which I am going to mention as a path to start the career in data science and machine learning, I think you are smart enough to skip those steps and jump to the next steps 🙂
One more important thing to point out which programming language to use R vs Python?
My one liner answer would be to go for Python. It doesn’t mean R is not resourceful. However Industry demands Python more and after all we want to get a job that is the first and foremost important thing. There are pros and cons of both Python and R like R is more friendly when you deal with Statistical Modelling and Python is more used when it comes to advance Machine Learning and Artificial Intelligence tasks. So it would be better to start with Python and later once you have time and want to explore more then you are okay to use R, python or any other language which you feel more suitable for a particular task.
Enough talking, now let’s understand what the key steps to start with are:
Note: This area is very vast and there is no end of learning, so I am only pointing topics which are enough to get you started and grab you a job.
Step 1: Understand basics of statistics:
- Basic Terminologies used in statistics
- Central Tendencies (Mean, Median, Mode etc.)
- Measures of Variability like Variance and Standard Deviation
- Basics of Probability
- Importantly Conditional Probability and Bayes’ Theorem
- Develop understanding on Probability distribution
- Central Limit Theorem
- Hypothesis Testing
- Confusion Matrix
Once you have good understanding of basic statistics which is going to help you realize Data Science and Machine Learning concepts, you are good to jump to the next step that is learning Python.
Step 2: Python
- Learn basics of Python
- Get familiar with Jupyter Notebook which will be your IDE for all your ML and AI work.
- Concentrate on pandas and numpy and get a hands on, on these two topics as they are used in every ML problem.
No need to invest lot of time here as once you start next step that is Machine Learning and start applying those concepts, you will get used to of python code, packages and libraries used. So not to worry much.
Step 3: Machine Learning
- Exploratory Data Analysis
- Data Cleaning
- Feature Engineering
- Principal Component Analysis
- Regression vs Classification
- Regression Analysis
- Linear, Multi-Linear and Logistic Regression
Now as you know one regression algorithm that is linear / multi-Linear regression and one classification that is logistic regression, you should start practicing problems on ML. so from now on wards, after finishing conceptual understanding of each new algorithm do make sure to solve at least one problem on that algorithm. For problems you can refer Kaggle or UCI Machine Learning repository where you will get lot of data sets and problem statements defined.
- Imp concepts in ML like Over fitting, Regularization, Correlation, Bias and Variance trade-off
- Performance Measures
- Clustering Techniques
- Association Rule Mining
- K-Nearest Neighbors Algorithm
- Decision Trees and associated terminology like entropy, information gain etc.
- Random Forest, Bagging and Boosting
There are more advanced algorithms which you can learn later for now these are more than enough.
Step 4: Solve Problems available on Kaggle
This step is very important. Because until you solve real problems, you will not have faced real challenges which arise during problem solving. These challenges no one will tell you which teaching the concepts even if someone is telling you will not understand properly until you yourself face and solve them.
Solving problems will give you confidence.one more very important advantage of this step is you will be able to answer questions which are interviewer ask you to check your practical knowledge.
there is one more repository where you can find many data sets for practice purpose. those are really nice data sets collected from real world problems. you can find it here
Step 5: Take part in Online, offline DS/ML/AI Hackathons
This step also has equal importance as the step 4. Here you get chance to interact with other professionals working on same areas. In this way you learn different methods of solving the problems. Also you get to know what is happening in the industry in this field and what recruiters are looking for.
Step 6: Start Applying for Jobs
At this point you have enough understanding of Data Science and Machine Learning concepts. So you are good to go ahead and start applying your dream jobs. However, as you know this field is getting more attention now and recruiters are mentioning more catchy and buzz words from artificial intelligence and deep learning like image processing, speech recognition, bot implementation, NLP etc. in job description. Which sometimes scares us and then we hesitate to apply. So my suggestion would be to have some high level understanding of those AI concepts also however, this should not stop you in searching and applying for the jobs because there are enough number of jobs which require basic statistics and Machine learning only which you have already learnt in above steps.
Step 7: Deep Learning
Coming to AI and Deep learning, there are many fields in the study of AI, so my suggestion would be to choose one among them based on your interest and start practicing. Just get top level overview of other areas and that should be enough. As recruiters want expertise in one of the areas based on job requirement.
Before that there are some basic concepts which are must to understand.
- Understand well how Neural Network works.
- Basic terminologies like importance of different layers, loss function etc.
- Understand how deep learning models works
Now I am mentioning different fields in AI. You can choose one from them and start exploring.
- NLP: deals with textual data. Examples are chat bots, Q & A generation etc.
- Image Processing
- Speech recognition
- Audio/Video analysis examples audio to text, video to images and text etc.
Note to Experienced Professionals
Understanding this point is very important and this is going to be a differentiating factor between you and a fresher or less experienced professional. Because you need to convince the recruiter why they will pay more to you when a fresher can do the same job.
Professionals who have 5+ years of experience and want to switch their career into data science and machine learning, here is my 5 pointer guide to prepare for interview.
- Know your existing skills and refresh them. Not from coding front however more on adopting the best practices and way of working.
- Think of what extra you bring to the job profile in comparison to a fresher or up to 3 years experienced professionals.
- Recall what processes and good practices you are following in your current role and how those can be benefited in the new role. Don’t forget to mention these in your resume and during interview.
- Think if you have experience in directly communicating with stake holders, and discussing the requirements. This can be very good differentiating factor between you and a fresher.
- Think of which domain you are working on currently example semiconductor, Healthcare, e-commerce etc. And you will get advantage in getting job on same domain. As you have good understanding of the domain and will help in creating the new use cases and identifying the potential pain areas which can be solved using Artificial Intelligence.
So to conclude let me summarize all the steps which you can follow to get your dream job in data science.
First question yourself how much you know about Data Science and Machine Learning. Then start learning those concepts which you think you are lacking. So the typical steps which in general we all can follow to make career in Data Science would be as following:
- Start understanding Basics of Statistics.
- Learn Python
- Explore some basic ML algorithms like Regression, KNN, Decision Tress and Random Forests
- Try solving problems available in Kaggle and UCI Machine Learning Repository.
- Participate on online Hackathons
- Start Applying for Jobs
- Learn how Neural Network works
- Understand Deep Learning Architecture
- Based on your interest learn any one from NLP, Image processing, Speech recognition, Audio/Video analysis etc technologies.
So that’s all about how to start a career in Data Science and Machine Learning.
If you are an aspiring data scientist or an experienced professional who is trying to make his career in Data Science, then you must visit E-network. Where we focus on high-quality interactive mock interview sessions and help you to Quick-start your Data Science and Machine Learning journey by Preparing a learning road-map, providing study material, Resume Building, suggesting Best training institutes and provide practice problems with their solutions and many more…
Feel free to contact us for more details and discussions.
Thank you for reading 🙂