Introduction to Data Science 4/10/23
Data Manipulation
• Real world data sources
-New ways of thinking about data
-Python PANDA data frame
-Python related to practical (data analytics)
Real World Data
• Customer Relationship Management (CRM) Systems
-They are not just about politeness
-They hold the info you have on a customer and inform how you can improve
their experience
-One-to-one and collective data (generating data sets = trends, sales, marketing
strategies)
CRM Data Points
• Insights
-Customer names, demographics, etc. = data set
-Purchase history details are great for data analytics
-Raised complaints are logged
• Customer Lifetime Value
-CLV = insights (not just simple questions) exploration, interrogation
-Literal columns of data lead to a number based on purchase history, spending
history etc.
-Feedback/Sentiment (online presence, reviews of product) allows analysis of
words used by customers
-This can inform marketing strategies i.e., data driven decisions
Social Media Platforms
• Social media
-There has been lots of development in attitudes towards social media
-Analysis allows you to build a profile of your target audience
-Potential customers can also be identified (moving on from CRM)
Social media data points
• Users
,-Profile data = target audience
-Popularity/reach of content means you can see where particular individuals are
key
-Parse text for key positive/negative comments/words (sentiment analysis)
-Politics/sociology can be useful for further tailoring to particular demographics
-Branching up different customers using algorithms for analysing the
connectedness of individuals
-Find the influencers and get them to market the product to their followers
-Privacy settings create the potential for the company to know you as a
customer, person and know where you are
-By leveraging these vast data points companies can gain an understanding of
their audiences
Sensor Networks and Internet of Things
• IoT devices
-Gas, electricity, sewage etc. = sensor data (enormous and valuable information)
-Consumption of electricity = clusters
-Sensor networks = manufacturing and healthcare (wearable devices)
-Useful for ageing population
IoT Data points
• Data collection
-Sensor data detects anomalies and targets someone to fix it
-Energy Consumption
-Equipment performance (optimization)
-Location tracking (i.e., item of inventory) = product location and destination
-Costs to transport inventory in a lorry (maximising inventory from point A to
point B so the lorry is always carrying something means less cost to company)
-Energy expenditure = geographical dispersion, understanding where costs are
located
-User behaviour is not JUST humans
-Radio Frequency ID tags (IoT devices) in Supply Chain Tracking
-Example = small beer company manufacturing IPAs:
- the beer left the gates in lorries, and they didn’t know where it was going
as there was no way to track the lorries’ location. The company wanted to track
where their inventory was, how much establishments are taking and how long it
takes them to get through inventory as this influences marketing decisions
, -Predictions = demand
Financial Data
• Points
-Banks use data analytics
-Stock market data (predicting trends in the stock market)
-Betting (predictions)
-How much money is available on demand informs how market analysts can
leverage financial data (fraud)
Financial Data points
-Not just individuals but a collective understanding
-Stock trades = time and value, which stock to buy, how much for etc.
-Trends = getting ahead of market surge (i.e., buy low, sell high)
-Risk Metrics = looking at the collective to establish whether the individual is a
good risk or bad risk
Web Analytics
• Websites and online platforms
-Generate data on a range of things up to large scale gaming platforms
-Tracking is not just text files anymore, there is lots of info
-A http request on a web application makes it possible to capture info about
clicks, searches and usage, along with user interactions with websites
Web Analytics Data Points
• Insights
-User engagement = what they are coming to this website for
-Is the website interactive, are they fascinated by the information presented, are
they strugging to find the info (different reasons people could be on a website)
-High exit bounce rate = quick visit (bad or good? Have they found what they
were looking for quickly or can they not find something)
-Companies want longer stays on their websites
-It is good to know whether a website is not enticing or engaging
-Cookies are helpful from a company’s perspective but users don’t want to be
tracked
-Click-through rate can be used to track ad performance
-Developers are also interested in web analytics
Data Manipulation
• Real world data sources
-New ways of thinking about data
-Python PANDA data frame
-Python related to practical (data analytics)
Real World Data
• Customer Relationship Management (CRM) Systems
-They are not just about politeness
-They hold the info you have on a customer and inform how you can improve
their experience
-One-to-one and collective data (generating data sets = trends, sales, marketing
strategies)
CRM Data Points
• Insights
-Customer names, demographics, etc. = data set
-Purchase history details are great for data analytics
-Raised complaints are logged
• Customer Lifetime Value
-CLV = insights (not just simple questions) exploration, interrogation
-Literal columns of data lead to a number based on purchase history, spending
history etc.
-Feedback/Sentiment (online presence, reviews of product) allows analysis of
words used by customers
-This can inform marketing strategies i.e., data driven decisions
Social Media Platforms
• Social media
-There has been lots of development in attitudes towards social media
-Analysis allows you to build a profile of your target audience
-Potential customers can also be identified (moving on from CRM)
Social media data points
• Users
,-Profile data = target audience
-Popularity/reach of content means you can see where particular individuals are
key
-Parse text for key positive/negative comments/words (sentiment analysis)
-Politics/sociology can be useful for further tailoring to particular demographics
-Branching up different customers using algorithms for analysing the
connectedness of individuals
-Find the influencers and get them to market the product to their followers
-Privacy settings create the potential for the company to know you as a
customer, person and know where you are
-By leveraging these vast data points companies can gain an understanding of
their audiences
Sensor Networks and Internet of Things
• IoT devices
-Gas, electricity, sewage etc. = sensor data (enormous and valuable information)
-Consumption of electricity = clusters
-Sensor networks = manufacturing and healthcare (wearable devices)
-Useful for ageing population
IoT Data points
• Data collection
-Sensor data detects anomalies and targets someone to fix it
-Energy Consumption
-Equipment performance (optimization)
-Location tracking (i.e., item of inventory) = product location and destination
-Costs to transport inventory in a lorry (maximising inventory from point A to
point B so the lorry is always carrying something means less cost to company)
-Energy expenditure = geographical dispersion, understanding where costs are
located
-User behaviour is not JUST humans
-Radio Frequency ID tags (IoT devices) in Supply Chain Tracking
-Example = small beer company manufacturing IPAs:
- the beer left the gates in lorries, and they didn’t know where it was going
as there was no way to track the lorries’ location. The company wanted to track
where their inventory was, how much establishments are taking and how long it
takes them to get through inventory as this influences marketing decisions
, -Predictions = demand
Financial Data
• Points
-Banks use data analytics
-Stock market data (predicting trends in the stock market)
-Betting (predictions)
-How much money is available on demand informs how market analysts can
leverage financial data (fraud)
Financial Data points
-Not just individuals but a collective understanding
-Stock trades = time and value, which stock to buy, how much for etc.
-Trends = getting ahead of market surge (i.e., buy low, sell high)
-Risk Metrics = looking at the collective to establish whether the individual is a
good risk or bad risk
Web Analytics
• Websites and online platforms
-Generate data on a range of things up to large scale gaming platforms
-Tracking is not just text files anymore, there is lots of info
-A http request on a web application makes it possible to capture info about
clicks, searches and usage, along with user interactions with websites
Web Analytics Data Points
• Insights
-User engagement = what they are coming to this website for
-Is the website interactive, are they fascinated by the information presented, are
they strugging to find the info (different reasons people could be on a website)
-High exit bounce rate = quick visit (bad or good? Have they found what they
were looking for quickly or can they not find something)
-Companies want longer stays on their websites
-It is good to know whether a website is not enticing or engaging
-Cookies are helpful from a company’s perspective but users don’t want to be
tracked
-Click-through rate can be used to track ad performance
-Developers are also interested in web analytics
