I have seen people struggling to make their career in Data Science . It is not really difficult while it is quite easy if you do the preparation strategically . I have seen people start learning and stop early because they do not have certain goals . This article is completely design for those who want to know How to prepare for Data Science job is 25 Days ?
Steps to prepare for Data Science job –
We will divide this in three chunks . First will cover 10 days and deal with basics . Second will be on machine learning . Last 5 days will be for domain libraries like – NLP , Computer vision etc .
Phase 1 (10 days) –
- I am choosing Python for your learning journey . Please visit this complete article for How to cover Python essential for Data Science in 5 Days ? This will guide you to finish or revise the essentials in this path . See ! Please do not miss the order of topics as one may be dependent on another . Day ( 1-5 )
2. Learn the IDE shortcuts and best python programming practices . You may learn and follow PEP-8 programming standard . These python coding standard is commonly adopted across the majority companies in Software Industry with Python language . ( Day 6 )
3. Give two day to learn / revise pandas . This is one of the data analysis library with Python . there are certain operations which are very important in pandas . You should have hands on their syntax like – reading from file , iterating data frame, value based on location , group by operations , merging etc .According to over plan I will give two days to cover these topics . Apart from videos tutorials points is good enough to cover each aspect very quickly here . Day ( 7-8 )
4. Numpy is another library which supports matrix operations in data science . Here you may create multi dimensional array . In order to read you may use tutorial points documentation ( recommended for job preparation ) .You may use our content as well if you need to take a overview only – Day (9-10 )
Numpy Tutorial : A Guide for Beginners (Creation, Conversion ,Indexing )
Phase 2 ( 10 days ) –
5. You should be able to do some sort of data visualization . You need to explore matplotlib for this . I will suggest a source to read edureka blogs . I will recommend you to practice this in 2 days at least .Here hands on knowledge is more important than overview . So please spend some time on drawing graphs etc . Day (11-12)
6. Revise the stats and probability theory also .Day (13-14 )
7. Go for some machine learning algorithms . Start with classification and regression . Then few algorithms on unsupervised machine learning . One more and most important advice is – Do not try to cover all possible algorithms in each topic but go to depth of whatever you are reading . Day ( 14-20 )
Phase 3 ( 5 days ) –
8 . As you know there are few advance field where machine learning and data science is excelling . Most of the industries are working in the same fields . Like NLP , Computer vision, predictive analytics etc . Here you have to master one and now its up to you . Each has its own set of frameworks now you have to practice them .
Day ( 21-25 )
Data Science Learner Team
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