How To Become A Data Scientist – The Complete Guide

How To Become A Data Scientist – The Complete Guide from Coding compiler. In this article, you will get all the answers to your questions, How to become a good data scientist? Should I learn R* or Python*? Or do both learn? Do I need to get a Ph.D.? Do I need a lot of math classes? What soft skills do I need to succeed? Let’s explore and start learning how to become a data scientist.

How To Become A Data Scientist.!

Introduction to how to become a data scientist

How to become a good data scientist? Should I learn R* or Python*? Or do both learn? Do I need to get a Ph.D.? Do I need a lot of math classes? What soft skills do I need to succeed? What is the project management experience? Which skills can be transferred? Where do you start?

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Data science is a hot topic in today’s scientific and technological world. Science is driving the development of many global trends, including machine learning and artificial intelligence.

In this article, we’ll walk through a series of steps to introduce data science-related knowledge so that product managers or business managers interested in data science can move beyond the first step toward data scientists or at least deepen their understanding.

Step 1: Define the problem statement

We’ve all heard this conversation: “View the data and tell me what you found.” This approach may work when the amount of data is small, the data is mostly structured, and the data is limited. But when we need to process a few gigabytes or terabytes of data, this approach can lead to endless, arduous inspection tasks and no results, because there is no problem to start.

Although science is powerful, it is not magic. Any invention in the scientific field can solve a problem. Similarly, the first step in using data science is to define a problem statement, a hypothesis that needs to be validated, or a question that needs to be answered. It may also focus on trends to be discovered, estimates and forecasts to make, and so on.

Let’s take the example of MyFitnessPal, a mobile app for monitoring health and fitness. I and a few of my friends downloaded the app a year ago and used it for almost a day. But in the past six months, most of us have been completely unused. If I am a product manager at MyFitnessPal, then one question I might want to solve is: How to improve customer engagement and retention?

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Step 2: Get the data

Today’s data scientists need access to data from multiple sources. These data may be structured data or unstructured data. The raw data we often get is unstructured and/or dirty data that needs to be cleaned and structured before it can be used for analysis. Most common data sources now provide an interface for importing raw data in R or Python.

Common data sources include:

• database
• CSV file
• Social media sources such as Twitter, Facebook, etc. (unstructured)
• JSON
• Web erasure data (unstructured)
• Web analysis
• IoT-driven sensor data
• Hadoop
• Spark
• Customer interview data
• Excel analysis
• Academic literature
• Government research literature and libraries, such as www.data.gov
• Financial data, such as financial data from Yahoo Finance

In the field of data science, common terms include:

• Observation or example. These can be viewed as horizontal database records from a typical database.
• Variables, signals, features. These are equivalent to fields or columns in the database. Variables can be qualitative or quantitative.

⇄ Observation or example	⇄ is equivalent to a row in the database.	⇄ example: Joe Allen of customer records.
	Variables, signals, features
	⇅ equivalent column
	⇅ example: Joe’s height.

Step 3: Clean up the data

Several terms are used to refer to data cleansing, such as data reprocessing, data preprocessing, data conversion, and data collation. These terms refer to the process of preparing raw data for data analysis.

As many as 70–80% of the work in data science analysis involves data cleansing.

Data scientists analyze each variable in the data to assess whether it is worthy of being a feature in the model. Adding a variable increases the predictive power of the model and is considered a predictor of the model. Such a variable is considered a feature of all these properties together to create a model feature vector. This analysis is called feature project.

Sometimes a variable may need to be cleaned or converted to be a feature in the model. For this reason w,e wrote the script, also known as re-processing script. > The script can execute a series of functions, such as:

• Rename variables (for readability and code sharing)
• Convert text (if “variable = “big” set variable = “HUGE”)
• Cut data
• Create new variables or transpose data (for example, age based on birthday)
• Add additional data to existing data (for example, get cities and states based on postal codes)
• Convert discrete numeric variables to continuous ranges (such as salary to salary range; age to age range)
• Date and time conversion
• Convert categorical variables into multiple binary variables. For example, categorical variables for regions (possible values for East, West, North, South) can be converted to four binary variables: East, West, North, and South, of which only one is suitable for observation. This approach helps create simpler connections in your data.

Sometimes the values of the data vary in number, making the information difficult to visualize. We can use the function scaling to resolve this problem. For example, consider the square foot of the house and the number of rooms. If we make the square footage of the home similar to the number of bedrooms, our analysis will be easier.

A series of scripts are applied to the data in an iterative manner until we get enough valid data to analyze. In order to continuously provide data for analysis, a series of data reprocessing scripts need to be re-run with new raw data. A data pipeline is a series of processing steps that apply to raw data to ensure that it is ready for analysis.

Step 4: Data Analysis and Model Selection

Now that we have valid data, we can analyze it. Our next goal is to familiarize yourself with data using features such as statistical modelling, visualization, and discovery-oriented data analysis.

For simple questions, we can use the average, the equivalent in progress, mode, minimum, maximum, mean, range, quartiles and other simple statistical analysis.

Supervised learning

We can also use supervised learning, whose data set allows us to access the actual values of response variables (dependent variables) for a specific set of characteristic variables (independent variables).

For example, we can look for trends (resigned=true) from actual data based on the time, qualifications, and positions of employees who have left the company, and then use these trends to predict whether other employees will also resign. Or we can use historical data to correlate trends between the number of visitors (independent variables or predictors) and the generated revenue (dependent or response variables). This association can then be used to predict the future revenue of the site based on the number of visitors.

The key requirements for supervised learning are the availability of actual values and a clear question that needs to be answered. For example, Will this employee leave? How much income is expected? Data scientists call it ” marking response variables for existing data. “

Regression is a common tool for supervised learning. Single factor regression uses one variable; multivariate regression uses multiple variables.

Suppose the unknown linear regression relationship between the factor and the response is a linear relationship Y = a + bx, where b is the x coefficient.

A portion of the existing data is used as training data, to calculate the value of the coefficients. Data scientists typically use 60%, 80%, or 90% (occasionally) data for training. Once for the training modelnumerical coefficients, then using the remaining data (also referred to as test data for testing), to predict the value of the response variable. In response to the difference between the predicted and actual values are known as an effective indicator of the error index test.

We explore the data in terms of scientific modelling is to minimize test error indicators, to improve the predictive power of the model in the following ways:

• Select a valid factor variable
• Write effective data rework scripts
• Choose the appropriate statistical algorithm
• Select the amount of test and training data you need

Unsupervised learning

Unsupervised learning is used when we try to learn the structure of the underlying data itself. It has no response variables. The data set is not marked and the original insight is not clear. We don’t know anything, so we don’t plan to predict anything!

This method is effective for exploratory analysis and can be used to answer the following questions

• Grouping. How many customer groups do we have?
• abnormal detection. Is this situation normal?

Analysis of variance (ANOVA) is a common technique used to compare the average of two or more groups. It is called ANOVA because “variance estimation” is the main intermediate statistic of the calculation. Use a variety of distance metrics to compare the averages of different groups, where Euclidean distance is a common method.

Analysis of variance for similar observations into groups also referred to as clusters. The observations may be appropriate predictor classified into these clusters.

Two common clustering applications are:

• Hierarchical clustering. A bottom-up approach. We start with separate observations and combine them with the closest observations. We then calculate the average of these grouped observations and combine these groups with the closest average. We will repeat this work until a larger group is formed. Distance metrics are defined in advance. This method is complex and does not apply to high-dimensional data sets.
• 18/5000 hierarchical variance analysis
• K-means clustering. Use the partitioning method.
  • Based on our intuition, we assume that the data has a fixed number of clusters.
  • We also assume that the starting centre of each cluster.
  • Then assign each observation to the cluster with the average of the closest observations
  • Repeat this step until all observations have been assigned to the cluster.
  • Now we recalculate the average of the cluster based on the average of all observations assigned to the cluster.
  • Reclassify the observations into these new clusters and repeat steps c, d, and e until they reach a steady state.

If the steady state is not reached, we may need to improve the number of clusters (ie, K) assumed at the beginning, or use another distance metric.

Step 5: Visualization and effective communication

The final cluster can be visualized for simple communication using tools such as Tableau* or graphics libraries.

Tips for data science practitioners

In my understanding of data science, I met practitioners working at companies like Facebook, eBay, LinkedIn, Uber, and consulting companies that are making effective use of data. This is some of the important advice I got:

• Know your data. It is important to fully understand the data and the assumptions behind it. Otherwise, the data may not be valid, which may result in a wrong answer, a solution to the error, or both.
• Understand the fields and issues. Data scientists must have an in-depth understanding of the business domain and the issues to be solved in order to extract the appropriate insights from the data.
• Ethics. Don’t sacrifice data quality for assumptions. The problem is often not ignorance, but our preconceived notion!
• A larger data set always provides better insight, which is a paradox. While larger data volumes are statistically significant, large data sets can also introduce greater noise. It can often be seen that the R-squared value of a larger data set is less than the R-squared value of a smaller dataset.
• Although data science itself is not a product, it can support outstanding products that solve complex problems. Product managers and data scientists who communicate effectively can be powerful partners:
  • The product manager first describes the business issues to be resolved, the questions to be answered, and the limitations to be discovered and/or defined.
  • Data scientists have extensive expertise in machine learning and mathematics, focusing on the theoretical aspects of business problems. Modern data set is used to perform data analysis, conversion, model selection and validation, in order to establish applied to business problems theoretical basis.
  • Software engineers are committed to implementing theory and solutions. He or she needs a strong understanding of the mechanisms of machine learning (Hadoop clustering, data storage hardware, and writing production code, etc.).
• Learn programming languages. Python is the easiest to learn; R is considered the most powerful language.

Common data science tools

R

R is a tool that many data scientists like and has a special place in academia. This tool deals with data science issues from the perspective of mathematicians and statisticians. R is a rich open source language with approximately 9,000 additional packages. The tool used to program in R is called R Studio*. Although R’s adoption rate in the enterprise has been steadily increasing and its popularity is partly due to its rich and powerful regular expression-based algorithms, R is more complicated to learn.

Python

Python is gradually becoming the most widely used language in the data science community. Like R, it is also an open source language used primarily by software engineers who see data science as a tool for using data to solve real business problems for customers. Python is easier to learn than R because Python emphasizes readability and productivity. Python is also more flexible and simple.

SQL

SQL is the basic language used to interact with the database and is the language required for all tools.

Other tools

• Apache Spark provides Scala*
• MATLAB* is a mathematical environment that has long been used in academia. It provides an open source version called Octave*
• Java* for Hadoop environments

What soft skills are needed?

Here are some of the important soft skills you need to have, and you may already have many of them.

• communication. Data scientists cannot sit alone in the office to encode Python programs. Data science requires you to work with the team. You need to communicate and build relationships with executives, product owners, product managers, developers, big data engineers, and NoSQL* experts. Your goal is to understand what they are trying to build and how data science and machine learning can help.
• guide. As a data scientist, you need to have excellent coaching skills. You are not an independent contributor to the company; you are the best partner of the CEO to help shape the company, which is based on data science to shape products and areas. For example, based on your data science results, you provide a prospective analysis of the management team and recommend that the company launch dark green shoes in Brazil; if this product is placed in Silicon Valley, it will fail. Your findings can save the company millions of dollars.
• Excellent story narrator. A good data scientist is a good storyteller. In the process of developing a data science project, you will have a large amount of data, theory and results. Sometimes you feel like you are lost in the ocean of data. If this happens, please take a step back and think: What goals do we want to achieve? For example, if your audience is CEO and COO, they may need to make a decision within a few minutes based on your presentation. They don’t care about your ROC curve, nor do they view up to 4 terabytes of data and 3,000 lines of Python code.
• Your goal is to provide direct advice based on reliable prediction algorithms and accurate results. We recommend creating 4 to 5 slides that clearly tell the story in the slides with reliable data and research. Visualization. Good data scientists need to use visualization to communicate results and recommendations. You cannot let someone read a 200-page report. You need to render using pictures, images, charts, and graphics.
• Thinking mode. A good data scientist has a “hacker” mentality (the “hacker” here is derogatory) and constantly looking for patterns in the data set.
• Like data. You need to use the data and mine the value from it. Of course, you can use a lot of tools to get a more complete picture of the data, but you can get a lot of information by browsing roughly.

What can I be?

It is time to decide. What type of data scientist should I be?

• Understand the pipeline. You need to start somewhere. You can be a Python developer in a data science project. You can collect input data from sources such as logs, sensors, CSV files, and more. You can write scripts to use and import incoming data. Data can be either static or dynamic. You may decide to become a big data engineer using technologies like Hadoop or Hive*. Or, you might decide to become a machine learning algorithm expert, the one who has the relevant skills and knows which algorithm is best for which type of problem. You may be a math genius, free to use out-of-the-box machine learning algorithms and modify them according to your needs. You may become a data persistence expert. You can use SQL or NoSQL technology to persist/provide data. Or, you might become a data visualization expert, using tools like Tableau to build dashboards and data cases. So check the pipeline above: from import to visualization. Create a list of opportunities. Such as “D3 expert, Python script expert, Spark master” and so on.
• Check out the job market. Check out the various job portals to understand current needs. How much work is available? What is the highest demand for work? What is the salary structure? 
• know yourself. You already know the types of pipes and jobs available. It’s time to think about yourself and your skills. What do you like best, what experience do you have? Do you like project management? What about the database? Think about your previous success stories. Do you like to write complex scripts to associate and manipulate data? Are you working on visualization and are good at creating compelling presentations? Create a “list that likes to work.” For example, “Like coding, like scripts, like Python.”
• Create a match. Create a list of opportunities based on your favourite work list and continue with the program. Data science is a sea of information. Stay focused!!