Data analysis involves cleaning, transforming and modeling data to extract useful information for making business decisions. It involves gathering past data or memories to analyze what happened previously or what could happen from different decisions in order to make informed choices. There are various tools that can help users process, manipulate and analyze relationships in data to identify patterns and trends. Major techniques of data analysis include text analysis, statistical analysis, diagnostic analysis, predictive analysis and prescriptive analysis. Statistical modeling applies statistical analysis to data to understand relationships between variables, make predictions and visualize data for stakeholders. Learning statistical modeling helps in choosing the right model, preparing data for analysis, and communicating findings to different audiences.

1. Data Analysis
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful
information for business decision-making. The purpose of Data Analysis is to extract useful information
from data and taking the decision based upon the data analysis.
A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking
about what happened last time or what will happen by choosing that particular decision. This is nothing
but analyzing our past or future and making decisions based on it. For that, we gather memories of our
past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for
business purposes, is called Data Analysis.
Why Data Analysis?
If your business is not growing, then you have to look back and acknowledge your mistakes and make a
plan again without repeating those mistakes. And even if your business is growing, then you have to look
forward to making the business to grow more. All you need to do is analyze your business data and
business processes.

2. Data Analysis
Data Analysis Tools:
Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and
correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is
a complete list of tools used for data analysis in research.

3. Types of Data Analysis: Techniques and Methods
There are several types of Data Analysis techniques that exist based on business and technology.
However, the major Data Analysis methods are:
Text Analysis: NLP
Statistical Analysis: mean, mode, median, correlation, regression
Diagnostic Analysis: Report blood test
Predictive Analysis
Prescriptive Analysis

4. Types of Data Analysis: Techniques and Methods
Text Analysis
Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover a
pattern in large data sets using databases or data mining tools. It used to transform raw data into business
information. Business Intelligence tools are present in the market which is used to take strategic business
decisions. Overall it offers a way to extract and examine data and deriving patterns and finally
interpretation of the data.
Statistical Analysis
Statistical Analysis shows "What happen?" by using past data in the form of dashboards. Statistical
Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set
of data or a sample of data. There are two categories of this type of Analysis - Descriptive Analysis and
Inferential Analysis.
Descriptive Analysis
analyses complete data or a sample of summarized numerical data. It shows mean and deviation for
continuous data whereas percentage and frequency for categorical data.
Inferential Analysis
analyses sample from complete data. In this type of Analysis, you can find different conclusions from the
same data by selecting different samples.

5. Types of Data Analysis: Techniques and Methods
Diagnostic Analysis
Diagnostic Analysis shows "Why did it happen?" by finding the cause from the insight found in Statistical
Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your
business process, then you can look into this Analysis to find similar patterns of that problem. And it may
have chances to use similar prescriptions for the new problems.
Predictive Analysis
Predictive Analysis shows "what is likely to happen" by using previous data. The simplest data analysis
example is like if last year I bought two dresses based on my savings and if this year my salary is increasing
double then I can buy four dresses. But of course it's not easy like this because you have to think about
other circumstances like chances of prices of clothes is increased this year or maybe instead of dresses you
want to buy a new bike, or you need to buy a house!
So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting
is just an estimate. Its accuracy is based on how much detailed information you have and how much you
dig in it.
Prescriptive Analysis
Prescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a
current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because
predictive and descriptive Analysis are not enough to improve data performance. Based on current
situations and problems, they analyze the data and make decisions.
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6. Data Visualisation
Data visualization is the graphical representation of information and data. By using visual
elements like charts, graphs, and maps, data visualization tools provide an accessible way to see
and understand trends, outliers, and patterns in data.
In the world of Big Data, data visualization tools and technologies are essential to analyze
massive amounts of information and make data-driven decisions.
Data visualization is another form of visual art that grabs our interest and keeps our eyes on the
message. When we see a chart, we quickly see trends and outliers. If we can see something, we
internalize it quickly. It’s storytelling with a purpose. If you’ve ever stared at a massive
spreadsheet of data and couldn’t see a trend, you know how much more effective a
visualization can be.
The different types of visualizations
Common general types of data visualization:
Charts, Tables, Graphs, Maps, Infographics, Dashboards
More specific examples of methods to visualize data:
Area Chart, Bar Chart, Box-and-whisker Plots, Bubble Cloud, Bullet Graph, Cartogram, Circle
View,
Dot Distribution Map, Gantt Chart, Heat Map, Highlight Table, Histogram, Matrix, Network,
Polar Area, Radial Tree, Scatter Plot (2D or 3D), Streamgraph, Text Tables, Timeline, Treemap,
Wedge Stack Graph, Word Cloud, And any mix-and-match combination in a dashboard!

7. Data Exploration
Data exploration is the initial step in data analysis, where users explore a large data set in an unstructured
way to uncover initial patterns, characteristics, and points of interest. This process isn’t meant to reveal
every bit of information a dataset holds, but rather to help create a broad picture of important trends and
major points to study in greater detail.
Data exploration can use a combination of manual methods and automated tools such as data
visualizations, charts, and initial reports.
This process makes deeper analysis easier because it can help target future searches and begin the
process of excluding irrelevant data points and search paths that may turn up no results. More
importantly, it helps build a familiarity with the existing information that makes finding better answers
much simpler.
Many times, data exploration uses visualization because it creates a more straightforward view of data
sets than simply examining thousands of individual numbers or names.
In any data exploration, the manual and automated aspects also look at different sides of the same coin.
Manual analysis helps users familiarize themselves with information and can point to broad trends.
These methods are also by definition unstructured so that users can examine a whole set without any
preconceptions. Automated tools, on the other hand, are excellent at pruning out less applicable data
points, reorganizing data into sets that are easier to analyze, and scrubbing data sets to make their
findings relevant.

8. What Can I Use Data Exploration For?
In any situation where you have a massive set of
information, data exploration can help cut it down to a
manageable size and focus efforts to optimize your
analysis.
Most data analytics software includes visualization
tools and charting features that make exploration at the
outset significantly easier, helping reduce data by rooting
out information that isn’t required, or which can distort
results in the long run.
By taking the time to perform a real exploration of your
data along with visualization tools, you can also start
finding correlations, patterns, and determine if a certain
path is worth researching, or if the information is less
usable.
Data exploration can also assist by reducing work time
and finding more useful and actionable insights from the
start alongside presenting clear paths to perform better
analysis.

9. Statistics for Model Building
Statistical modeling is the process of applying statistical analysis to a dataset. A statistical model is a
mathematical representation (or mathematical model) of observed data.
When data analysts apply various statistical models to the data they are investigating, they are able to
understand and interpret the information more strategically. Rather than sifting through the raw data, this
practice allows them to identify relationships between variables, make predictions about future sets of
data, and visualize that data so that non-analysts and stakeholders can consume and leverage it.
“When you analyze data, you are looking for patterns,” says Mello. “You are using a sample to make an
inference about the whole.”
In regression analysis, model building is the process of developing a probabilistic model that best
describes the relationship between the dependent and independent variables. The major issues are
finding the proper form (linear or curvilinear) of the relationship and selecting which independent
variables to include. In building models it is often desirable to use qualitative as well as quantitative
variables.

10. Statistics for Model Building
3 Reasons to Learn Statistical Modeling
1. You will be better equipped to choose the right model for your needs.
There are many different types of statistical models, and an effective data analyst needs to have a comprehensive
understanding of them all. In each scenario, you should be able to identify not only which model will help best answer
the question at hand, but also which model is most appropriate for the data you’re working with.
2. You will be better able to prepare your data for analysis.
Data is rarely ready for analysis in its raw form. To ensure your analysis is accurate and viable, the data must first be
cleaned up. This cleanup often includes organizing the gathered information and removing “bad or incomplete data”
from the sample.
“Before any statistical model can be completed, you need to explore [and], understand the data,” says Mello. “If there is
no quality [in the data], then you can’t really derive any insights from it.”
Once you know how various statistical models work and how they leverage data, it will become easier for you to
determine what data is most relevant to the question you are trying to answer, as well.
3. You will become a better communicator.
In most organizations, data analysts are required to communicate their findings with two different audiences. The first
audience consists of those on the business team who don’t need to understand the details of your analysis, but simply
want to know the key takeaways. The second audience consists of those who are interested in the more granular
details; this group will want both the list of broad conclusions and an explanation of how you reached them.
Having a thorough understanding of statistical modeling can help you better communicate with both of these
audiences, as you will be better equipped to reach conclusions and therefore generate better data visualizations, which
are helpful in communicating complex ideas to non-analysts. Simultaneously, a complex understanding of how these
models work on the backend will allow you to generate and explain those more granular details when necessary.