According to data from glassdoor, data scientists make an average of $117,345 a year, with starting salaries ranging from $50,000 to $90,000.Cognitive styles and learning strategies with everyone from facebook and microsoft to uber and airbnb constantly looking for talented data scientists, undoubtedly here’s where the money’s at.Cognitive styles and learning strategies so if you have a knack for numbers and looking to capitalize on the massive growth in this industry, you too could kick start a career in data science today.Cognitive styles and learning strategies the first thing you need is an aptitude for math. In fact, most data scientists possess advanced degrees in mathematics, statistics or other related fields.Cognitive styles and learning strategies

The next thing you need is a level of proficiency in computer programming, primarily working around languages that help compute all that data and make sense of it.Cognitive styles and learning strategies now this is where python comes in. R and python are the most commonly used programming languages for data science, out of which python is the one that’s largely preferred by professionals.Cognitive styles and learning strategies the thing about python is that it is a general purpose back-end programming language. It is easy to learn and get started with. It has a marked proficiency in analytical and quantitative computing.Cognitive styles and learning strategies you can tell that this is one language you can count on when you hear that it has been used by the likes of youtube and google. Python in data science

cognitive styles and learning strategies

So now you know that learning python can be the ultimate boost you’ll need to get your data science career established for success. So today, let’s talk about how you can learn python and leverage it to build a rewarding career for yourself.Cognitive styles and learning strategies here’s all the information you’ll ever need, to get a grip on what you need to learn, where to learn it from and how to put it to good use. Step 1: learn your way around python

cognitive styles and learning strategies

Now, the important thing here is that you shouldn’t get too caught up in learning python and focus on becoming a true blue python expert. It is more important to get generally comfortable with the language and do enough practice to learn your way around it.Cognitive styles and learning strategies once you’ve done that, it is best to move on to the next step instead of lingering too long on the aspects of language. Instead, apply what you learn and focus on building data structures, data types, loops, comparisons, imports, functions, conditional statements, comprehensions and a dozen other things that pertain to data science in particular.Cognitive styles and learning strategies step 2: get comfortable with python’s data science libraries

Having learnt the ropes of python, it’s time to learn the strings of python’s data science libraries.Cognitive styles and learning strategies these help make complex tasks easy and take most of the mundane coding off your hands. The result is better models with faster, lesser code and more time spent on innovating your model and solving problems.Cognitive styles and learning strategies learn your way around the biggest, most prominent ones first, such as numpy and scipy for scientific and numeric computation. These two provide you with several pre-compiled functions and data structures for quick and efficient computation.Cognitive styles and learning strategies

Then there is PANDAS, the python data analysis library that helps you add data structures for practical analysis and works smoothly even with incomplete, unorganized, messy and unstructured data.Cognitive styles and learning strategies another wildly popular library called matplotlib helps with the visualization aspect by producing quality figures like plots, histograms, scatterplots, bar charts and more and even lends hardcopy formats to present interactive data.Cognitive styles and learning strategies there are numerous other libraries that will help augment the power of python in developing robust data science applications. You can check out this post by kdnuggets for a detailed anthology of all fantastic data science libraries for python.Cognitive styles and learning strategies step 3: get to work and practice. Practice, practice

Too much learning is no good without an equal and simultaneous amount of doing. So pick a project you’re comfortable with and put what you learnt to good use.Cognitive styles and learning strategies drive and motivation along with technical soundness are of essence here. To keep yourself motivated as well as updated, stay current on the top community blogs and forums.Cognitive styles and learning strategies attend meetups and socialize with the python data science community to keep updated.

Entering kaggle competitions is a great way to not only challenge yourself and learn awesome new stuff but also, any victories will add to your portfolio in a big way.Cognitive styles and learning strategies the great thing about kaggle competitions is that you don’t have to hit the familiar roadblock of not having an amazing idea. These competitions keep the idea stream running so you can just keep coding.Cognitive styles and learning strategies

Everything you develop, be it for a kaggle competition or a personal project can go into a portfolio for potential employers to see. As long as you can show finesse with several datasets, deliver insights and show your desire to learn as well as your skill, your portfolio doesn’t need a particular theme.Cognitive styles and learning strategies show your diversity and proficiency. Everything from a small practice project to a prize winning entry can go into your portfolio. This could easily double as your resume when applying for data science jobs down the line.Cognitive styles and learning strategies show your familiarity with new data structures, bigger statistics, insights and models. Step 5: advance your skills

One lifetime is probably too small a thing to learn all of data science.Cognitive styles and learning strategies that is why it is important to make every project worthwhile. To truly build a rewarding career that keeps going higher, learn advanced data science techniques.Cognitive styles and learning strategies learn clustering, regression, classification and stay on top of emerging trends in data science. Always keep learning and experimenting. Conclusion

cognitive styles and learning strategies

Data science is undoubtedly one of the most exciting careers of our generation. With the amount of technology that surrounds us from smart devices to iot appliances, data is truly overflowing.Cognitive styles and learning strategies companies pay top dollars to unravel trends about customer behavior from this data. If you aspire to make big on this trend, the above steps will help set a solid foundation on which you can build a rewarding career in data science.Cognitive styles and learning strategies just keep on learning and never stop applying what you learn.