1. Business Understanding:

While working with business owners to understand what it does, what kind of decision they are going to make, and for which purpose the data is being analyzed, this data analyzing process started with a question, lots of people think that the data can be analyzed by using the data set, availability of the data set is sufficient to analyze any kind of pattern, as per understanding there is no data set for analyzing the data all we need it the questions define the data sets itself, the only challenge, in this case, is while answering the one questions another question can be pop up but it is ok, it more than actually a part of data analyzing process.

2. Acquire the Raw Data:

This is the step where after defining the question, data is collected from different sources such as data warehouse, logs, and data set to answer those question, row data is queried to answer the questions but this is not the row data set, instead, we need to call it row data because it is not exactly in the form of where we want it to analyze.

3. Extract the Data:

This is the step where data is extracted to create a final data set. that will allow us to lead the further analyzing process this is a clean data set. SQL is used for extracting the data from the database. the database which is queried to extract the data having several rows exceeding 1 Million. where database query languages like SQL enables an Analyst to analyze and transform data easily. SQL is the first thing you should learn as it enables you to work on the dataset.

4. Transform the Data:

Data transformation is the process of converting the data or dataset from one state or structure to another state structure, it is the fundamental state of data integration where the data collected from different sources have been integrated into particular structured data in such a manner that it can be used at a destination for analysis process this process is known as ETL(Extract Transform Load). The data transformation process refers to detecting and understanding the data in its original structured or source format. This is usually achieved with the help of algorithms which is implemented by using data analysis and profiling tool. This step helps you decide what needs to happen to the data to get it into the desired or requested format. Generally, R or Python language enables you to perform data transformation on large or complex data that is coming from the source.

5. Data Visualization:

After building or creating the datasets, we need to visualize data to develop our Hypotheses or Insights to explore and evaluate the data. Tableau/SaaS (data visualization application) allows us to visualize large rows of columns of data in both structured and unstructured databases and easily bring insights/ meaningful patterns out of the dataset.

6. Statically Analysis:

It is the important aspect of data analysis which summarizes the data and its understanding in terms of models and graphs apart from this it also explains how the data is related to the underlying real world. the statical analysis is also used to identify the pattern or trends for predictive analytics which helps to make the business decision, it also helps to determine the statical significance of the data set.

7. Data Model Development:

Industries are extremely interested to deploy model which has predictive capabilities, data model development consists of the definition of model goals, the concept of the problem, and its translation into a computational model.
The right modeling enables you to create a statistical model to reject any invalid or null hypothesis, the modern application plays an important role in handling the mathematical complexity. Vendors are developing software as services such as table and SAS to make the analysis process easier and easier by building models using automated predictive modeling tools designed for business analysts. analytics professionals are utilizing machine learning algorithms from open-source marketplaces or model-building APIs to build a predictive application model.

8. Recommendations/Report/Story:

This is the final step of the data analytics process where the analysis decision is summarized and the result or consequences of the analysis process is represented in terms of story, report, recommendations, and PPT, tableau and SAS application plays an important role to summarize the analysis process via a report or story building, this report includes:

• Customer/Industries centric outcomes.
• Strategy and decision tree for the industries.
• Identification of business priority.
• Identification of target audience or consumers for the products.
• Business case based on measurable outcomes.