Data science is a crucial part of several industries nowadays and is one of the most discussed topics in IT circles. This stream has grown over the years and businesses have started implementing data science techniques to grow their business and increase customer satisfaction. Here we’ll learn what data science is and how to become a data scientist.
Data science is the domain of study that deals with massive volumes of data using modern tools and methods to find invisible patterns, meaningful information and make business decisions and uses complex machine learning algorithms to build analytical tools.
Data science’s lifecycle consists of five stages:
- Data Acquisition, Data Entry, Signal Reception, Data Extraction.
- Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture.
- Data Mining, Clustering/Classification, Data Modeling, Data Summarization.
- Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle.
- Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports. If you go for data visualization mostly Power Bi and tableau plays the important tools.
Prerequisites for Data Science
Following technical concepts, you should know about before starting to learn what is data science.
1. Machine Learning
2. Modeling
3. Statistics
4. Programming
5. Databases
Data scientist may do the following tasks:
- Discover patterns and trends in datasets to get insights.
- Create forecasting algorithms and data models.
- Improve the quality of data or product offerings by utilizing machine learning techniques.
- Distribute suggestions to other teams and top management.
- In data analysis, use data tools such as R, SAS, Python, or SQL.
- Top the field of data science innovations.
A data scientist scrutinizes business data to extract meaningful insights and he solves business problems by following ways
- Data scientist determines the problem by asking the right questions and gaining understanding.
- Determines the correct set of variables and data sets.
- Collect structured and unstructured data from various disparate sources
- When the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis.
- Analyze and identify patterns and trends.
- Interprets the data to find opportunities and solutions.
- Results and insights to share with the appropriate stakeholders.
- Data Scientist Master’s Program
Data Science Tools
Following tools available to help the data scientist
- Data Analysis: Minitab, SAS, R Studio, Excel, MATLAB, RapidMiner
- Data Warehousing: Informatica/ Talend, AWS, Redshift
- Data Visualization: Tableau, Power BI
- Machine Learning: Spark MLib, Mahout, Azure ML studio
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