How to become a Data Scientist




Step 1
Step 1: Get a bachelor's degree in computer science or a related field. Data scientists need to understand the basic concepts of computer science and how they apply to data analysis. Most jobs require at least a bachelor’s degree, but it is possible to find entry-level positions with just a high school diploma or GED if you have relevant work experience.

Some universities offer undergraduate degrees specifically for data science; however, most do not yet offer those options. If your university does not offer an undergraduate program that works for you, focus on classes that will teach you about programming languages like Python or R (which are commonly used by data scientists), statistics and probability theory, algorithms and computational math skills such as linear algebra and calculus (which are also useful). Taking courses outside of these areas can help round out your knowledge base before pursuing a graduate degree in computer science at another institution.

Step 2
Learn Statistics

It’s no secret that data scientists rely heavily on statistics, so it makes sense that you would want to learn some if you want to pursue a career as a data scientist. Learning statistics will help you become familiar with the field and give you an edge over other applicants who lack similar skills. In addition to learning basic concepts like probability distributions and hypothesis testing, consider taking courses in multivariate analysis or statistical modeling if possible. Some schools offer introductory courses specific to data science while others may require students take more advanced classes like econometrics or time series analysis before becoming eligible for admission into their MS/PhD program (if offered). Once enrolled in one of these programs, be sure not just focus on mastering the material but also participate in research projects where your knowledge can be put into practice!

Step 3
Data scientist is a broad term

The role of a data scientist varies from company to company, team to team and department to department. If you are looking for an entry-level position then probably your job will be mostly related to data cleaning and preparing it for modeling and analysis. On the other hand if you have some experience in R or Python with machine learning algorithms then your job would involve building predictive models using those tools. It can include working on different aspects like exploratory analysis, model building, evaluation of models and communicating results to end users who are not technical experts in analytics space (Business Analysts).

A lot of data
To become a data scientist, you'll need to be able to collect, analyze and interpret data. You also need to know how to communicate your findings in an understandable way. Finally, you must be able to apply what you've learned from analyzing the data with practical applications.

In short: Data scientists are required to have more than just the ability to manipulate numbers on a spreadsheet. They must understand how the numbers relate back into real-world scenarios that matter for their employers or clients — whether those clients are individuals or businesses interested in improving their processes through analytics software tools like Tableau or Qlik Sense (two examples of BI tools).




What to study for being a Data Scientist

Linear Algebra
Linear algebra is the branch of mathematics concerning linear equations and systems of linear equations, such as those encountered in the study of geometry, physics, engineering, and economics. There are many other branches of algebra, for example group theory that could be useful for a data scientist. But linear algebra is fundamental to many branches of mathematics and science, as well as many areas of engineering and economics.

Network Analysis
Network analysis is a way of using data to understand complex systems. It’s used in business, medicine, sociology and other fields to better understand networks of people or things.

If you want to be a data scientist who uses social media data as part of your work — whether it’s analyzing the spread of information on Twitter or how people use Facebook — network analysis will come in handy. Consider: How do we know if someone’s tweet was retweeted? The answer is that every retweet has an associated edge on the social network graph that describes how it got there (i.e., which user retweeted it).

Network structures are often much more informative than raw counts when trying to understand social phenomena like the spread of ideas or diseases because they tell us about relationships between people or things in those networks.

Data Visualization
Data visualization is perhaps the most important skill to learn as a data scientist. It's often said that "data is the new oil," and in this era of information overload, visualizations are critical for extracting meaning from any given dataset.

To be sure, there are many different types of data visualization—like histograms, line charts and scatterplots—and there are even more ways to manipulate them once you're using them (i.e., by changing the y-axis ranges). But at its core, good visualization involves comparing related variables over time or space so that they can be interpreted quickly and easily by humans without complex statistical analysis tools like R or Python.

This process includes choosing which variables you want to compare (for example: income versus education level), deciding how they should be visually connected such as one continuous line or two separate lines on top of each other with different colors; using color schemes wisely so that readers can easily interpret what each means; selecting proper fonts for titles (usually sans serifs) as well as axes labels so that readers know exactly what numbers represent (e.g., $100K vs 10K); etc.). Once again: being able to choose these things requires practice because each decision has consequences!

Computational Statistics
The study of statistics is the science of learning from data. Computational statistics applies statistical methods to computation, and it's an important part of a data scientist's toolkit.

In order to understand why computational statistics is important, let's first consider what we mean by "statistics." Statistics is the collection, organization, analysis and interpretation of data in order to make inferences about populations or make predictions about future events. In other words, it's not just looking at numbers—it's also figuring out why those numbers matter.

A good example would be knowing how many people have purchased tickets for a movie before opening weekend (the population), then using that information to determine whether it will make money (the inference) or how many more people need to see that movie for it to break even (the prediction).

So why does this require computation? Well if you've ever taken any kind of math class before college level calculus then you probably remember learning how to use formulas like y=mx+b where m represents slope (upward or downward movement) while b represents an intercept point on the graph that indicates where y=0 horizontally speaking--meaning there is no upward nor downward movement when x changes between 0-100%.

Probability and Statistics for Machine Learning
Probability theory is the study of uncertainty. It allows you to make predictions, decisions, and draw conclusions given incomplete information. Probability theory is used in data science to model uncertainty and then use this understanding to make predictions, decisions, and draw conclusions. That's why you should learn it!

In probability theory we use a concept called the "likelihood". The likelihood tells us what is likely given a certain set of data or observations. For example: If I throw a die then there are six outcomes that could occur - 1-2-3-4-5-6 (1 being most probable). If I throw three dice then there are 216 possible outcomes (6 possible combinations for each die) but only one outcome has a sum of 7 (the highest number).

Time Series Analysis and Forecasting
Time series analysis is the study of temporal data—that is, data that has a time component. There are many different methods used in this field, but we'll focus on two: forecasting and seasonal adjustment. Forecasting refers to the method of predicting future values based on past information. Seasonal adjustment is a procedure used when there's some variation from one period (e.g., month or year) to another (e.g., season), so that you can compare numbers across periods for an accurate representation of what's happening over time.

Forecasting methods include exponential smoothing, ARIMA models, neural networks and support vector machines (SVMs). You can also use autoregressive integrated moving average (ARIMA) models for time series forecasting; these are popular because they're relatively straightforward to implement and easy to understand mathematically



These six areas are essential for data scientist.
As a data scientist, you will be expected to have a wide-range of skills and knowledge. The six areas below are essential for any aspiring data scientist.

Algorithms & Machine Learning: This field is focused on designing algorithms that can sift through large quantities of data to identify interesting patterns, make predictions about the future (such as how likely someone is to buy something), or solve other problems with computers.

Statistics & Probability: These two fields deal with using statistics (the study of large groups) and probability theory (the mathematical study of chance). Data scientists often use these tools when analyzing big data sets because they allow them to measure how well their models perform as well as understand what makes each customer unique so they can better target them for sales or advertising purposes.

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