What is Data scientist and the data science project?

 

What is a Data scientist?

Data Scientists are advanced analytical data experts who can perform various tasks with gathering and analyzing a large set of structured and unstructured data and come up with various solutions. A Data Scientist builds & runs code to create better models and solve complex problems. 

Data scientists began their carrier as mathematicians, statisticians, and business analysts. Data scientists were not available a decade ago. Suddenly their popularity raised when businesses thought about big data. Data scientists are using the below steps to perform the different projects.

1. Ask the right question 
2. Find the problem
3. Data gaining
4. Clean the data
5. Combine  and store data
6. Process and wrangle the data
7. Choose models and algorithms 
8. Apply data science techniques like machine learning and Artificial Intelligence.
9. Present final result through data analysis and data visualization
10.      Deploy the model

How to complete the data science project?

Data science is defined as extracting knowledge and information from data by using different technique and algorithm.

If you're passionate about data science, I will tell you how it really works. Let’s see how a day's inner life goes while working on a data science project well. It is very important to understand the business problem first in the meeting with the clients. Always asks relevant questions to understand and define objectives for the problem that needs to be tackled.

Understand Data science with an Analogy

·         ⇒Define problem

·         ⇒Data collection

·         ⇒Data preparation

·         ⇒Data exploring

·         ⇒Data analyzing

·        ⇒Data modeling

·        ⇒Data deploying

DEFINE PROBLEM

•          understand the problem
•          make the problem statement clear, goal-oriented and measurable
•          Identify centrally  objectives
•          Identify variables that need to be predicted

DATA COLLECTION

Data collections are two types

• ·    Primary data collection: for example, you want information about the average time that employees spend in a restaurant across companies. Here public data is not available. But data can be collected in various ways such as surveys, interviews of the employees, and monitoring. This is a time-consuming method.
• ·   Secondary data collection: this data is readily available in open source websites such as Kaggle, Github, Government censuses, Magazines, News articles etc. This is a less time-consuming method.

 DATA PREPARATION

• Data preparation and involves data cleaning and data transformation.     
• Inconsistent data types misspelled attributes, missing values, duplicate values, and so on.
• Modifies the data based on defined mapping rules in a project.
• ETL tools like talent and informatics are used to perform complex transformations that help the team understand the data structure.

 

EXPLORATORY DATA ANALYSIS

• Exploratory data analysis with the help of EDA.
• The selection of feature variables that use in model development
•  If you skip this step, you might end up choosing the wrong variables which will produce an inaccurate model.
• Thus exploratory data analysis becomes the most important step now.

DATA MODELING

•  Proceeds to the core activity of a data science project such as data modeling.
•  Applies machine-learning techniques like linear regression, SVM, decision tree.
• Trains the models on the training data set and tests them to select the best performing Python prefers a model for modeling the data.

·          However, R and SAS can be used as well.

VISUALIZATION AND COMMUNICATION

• ·    Do the business simply and effectively using tools like Tableau, Power bi, and QLIKVIEW to convince the stakeholders.
•      These tools help her to create powerful reports and dashboards.

DEPLOYS AND MAINTAIN THE MODEL

• Tests the selected model before deploying it in the production environment.
• This is the best practice right after successfully deploying it
• Use reports and dashboards to get real-time analytics and monitors and maintains the project's performance.

 This is the way to complete the data science project.

 

WHY DATA SCIENCE?

The daily routine of a data scientist is a lot of fun, interesting and challenging aspects. The first thing is, a data scientist is the number one job in this particular decade. We see exponential growth in artificial intelligence, data science, and all the fields related to it. There is a variety of jobs to offer to a data scientist like data analyst, machine learning engineer, deep learning.

Now it has come to the pace where this is the fastest-growing job in the world. There is going to be an amazing supply and demand chain. You know you can visit websites like Indeed, LinkedIn, and whatever is the local job portal. Wherever you stay across the world and you can get access to all of these jobs, guys, it's in the thousands. They are looking for people who are highly skilled, trained, and certified as well.