DATASCIENCE.pptx

INTRODUCTION OF
DATA SCIENCE
.

❖ Data Science is about data gathering, analysis and decision making.
❖ Data Science is about finding patterns in data, through
analysis, and make future predictions.
❖ By using Data Science, companies are able to make:
⮚ Better decisions (should we choose A or B)
⮚ Predictive analysis (what will happen next?)
⮚ Pattern discoveries (find pattern, or maybe hidden information in the
data)
DATA SCIENCE

� Statistics
� Domain Expertise
� Data Engineering
� Visualization
� Machine Learning
� Advanced Computing
DATA SCIENCE COMPONENTS

❖ The essential component of Data Science is Statistics.
❖ It is a method to collect and analyze the numerical data in a large
amount to get useful and meaningful insights.
❖ There are two main categories of Statistics:
⮚ Descriptive Statistics
⮚ Inferential Statistics
STATISTICS

❖ Visualization means representing the data in visuals such as maps,
graphs, etc.
❖ So that people can understand it easily.
❖ It makes it easy to access a vast amount of data.
❖ The main goal of data visualization is to make it easier to identify
patterns, trends, and outliers in large data sets.
VISUALIZATION

❖ Machine Learning acts as a backbone for data science. It means
providing training to a machine in such a way that it acts as a
human brain.
❖ Various algorithms are used to solve the problems. With the help
of Machine Learning, it becomes easy to make predictions about
future data.
For example :
❖ Social media platform , Face book
MACHINE LEARNING

❖ Domain expertise means the specialized knowledge or skills of a
particular area.
❖ There are various areas in data science for which we need domain
experts.
❖ The lesser we know about the problem, the more difficult it will be to
solve it.
DOMAIN EXPERTISE

❖ Data Engineering involves acquiring, storing, retrieving, and
transforming the data.
❖ The key to understanding data engineering lies in the engineering
part.
❖ Data engineers design and build pipelines that transform and
transport data into a format, and it reaches the Data Scientists or
other end users in a highly usable state.
DATA ENGINEERING

❖ Advanced computing involves designing, writing, debugging, and
maintaining the source code of computer programs.
❖ Advanced computing capabilities are used to handle a growing
range of challenging science and engineering problems, many of
which are compute- and data-intensive.
ADVANCED COMPUTING

❖ Data science is focused on making sense of complex datasets and
in building predictive models from those data.
❖ As such, it encompasses a wide array of different activities, from the
upstream processes of acquiring, cleaning and integrating data to
downstream processes of analysis, modelling and prediction.
FACETS OF DATA

⮚ Structured
⮚ Unstructured
⮚ Natural Language
⮚ Machine-generated
⮚ Graph-based
⮚ Audio, video and images
⮚ Streaming
THE MAIN CATEGORIES OF
DATA

There are many facets of data science, including:
⮚ Identifying the structure of data.
⮚ Cleaning, filtering, reorganizing, augmenting, and aggregating data.
⮚ Visualizing data.
⮚ Data analysis, statistics, and modelling.
⮚ Machine Learning.
⮚ Assembling data processing pipelines to link these steps.
⮚ Leveraging high-end computational resources for large-scale
problems.
FACETS OF DATA

⮚ Step 1: Frame the problem.
⮚ Step 2: Collect the raw data needed for your problem.
⮚ Step 3: Process the data for analysis.
⮚ Step 4: Explore the data.
⮚ Step 5: Perform in-depth analysis.
⮚ Step 6: Communicate results of the analysis.
DATA SCIENCE PROCESS

❖ The first thing you have to do before you solve a problem is to
define exactly what it is.
❖ You need to be able to translate data questions into something
actionable.
You should ask questions like the following:
❖ Who are the customers?
❖ Why are they buying our product?
❖ How do we predict if a customer is going to buy our product?
Frame the problem

❖ Once you defined the problem, you need data to give you the
insights needed to turn the problem around with a solution.
❖ This part of the process involves thinking through what data you
need and finding ways to get that data, whether it’s querying internal
databases, or purchasing external datasets.
❖ You can export the CRM data in a CSV file for further analysis.
Collect the raw data needed
for your problem

❖ Now that you have all of the raw data, you’ll need to process it
before you can do any analysis.
❖ Oftentimes, data can be quite messy, especially if it hasn’t been
well-maintained.
❖ You see errors that will corrupt your analysis: values set to null
though they really are zero, duplicate values, and missing values.
❖ It’s up to you to go through and check your data to make sure you’ll
get accurate insights.
Process the data for analysis

❖ When your data is clean, you should start playing with it.
❖ The difficulty here isn’t coming up with ideas to test, it’s coming up
with ideas that are likely to turn into insights.
❖ You have to look at some of the most interesting patterns that can
help explain why sales are reduced for this group.
Explore the data

❖ This step of the process is where you going to have to apply your
statistical, mathematical and technological knowledge and leverage
all of the data science tools at your disposal to crunch the data and
find every insight.
❖ You can now combine all of those qualitative insights with data from
your quantitative analysis to craft a story that moves people to
action.
Perform in-depth analysis

❖ It’s important that the VP Sales understand why the insights you
uncovered are important.
❖ Ultimately, you’ve been called upon to create a solution throughout
the data science process.
❖ Proper communication will mean the difference
between action and inaction on your proposals.
❖ You start by explaining the reasons behind the underperformance of
the older demographic.
Communicate results of the
analysis

DATASCIENCE.pptx

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