What is Data Analysis?
In the age of information, data is treated as a currency. Businesses need to collect and study data to make informed decisions.
Data Analysis is defined as a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
Within the context of software development the quantities, characters, or symbols on which operations are performed by a computer. These inputs may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.
While data or input could be facts or statistical information collected for analysis or reference, data analysis itself requires breaking data down into small separate components for examination.
What is Big Data?
Let’s examine what the popular term Big Data that we often come across, means.
Big data are large volumes of structured or unstructured data that businesses deal with every day. What organizations do with this data determines their strategic direction and big-picture goals.
Doug Laney coined the now-mainstream definition of big data as the three V’s:
Volume- Big data are characterized by its sheer volume, running into terabytes, exabytes, zettabytes, and petabytes. To give you a little perspective, a petabyte is one quadrillion bytes or the equivalent of about 20 million filing cabinets’ worth of text. Imagine Facebook where 500+terabytes of data is ingested every day.
Velocity
It refers to the speed at which this data is generated, collected, and analyzed.
Variety
The different types of data, structured, unstructured, and semi-structured.
Another addition to these 3vs is
Variability
The inconsistency displayed by data at times that needs to be considered while analyzing it.
Scope of Data Analysis Courses
With increasing digitalization, even traditional sectors are building an online presence by transforming their business processes and in doing so they are generating and receiving vast amounts of data that needs to be analyzed.
It also means that the data analysis field is still in the nascent stage and will only grow from this point onwards as it grows it will need people with relevant skills who can leverage this data to gather insights and help achieve business objectives. It will not just create new job opportunities but will also provide a scope for career progression. The importance of Data Analysis can be understood from the fact that educationists have argued in favor of its introduction at the secondary school level too.
Now is a good time to do a Data Analysis course and initiate a career in this growing field. On an average Data Analysis professionals in India are paid 50% more than their peers in the other IT service areas with important decision making role profiles to choose from.
Data Analysis helps one build other necessary skills for a successful career like:
- Problem-solving is an obvious skill that comes with engaging with data and leveraging it for solutions
- Communicating complex information by breaking it down into easy to understand simple components
- And the ability to do that leads to better communication and leadership skills
Data Analysis is a transferable skill which means it can be used in a different setting and for purposes other than analysis as well.
Data Analysis Projects for Beginners
To understand data, one needs to work with data and projects are an excellent way to do that. But before diving into a project, a project plan needs to be created which entails steps like:
- Identifying a topic
- Obtaining data
- Preparing the data
- Data Modeling
- Model Evaluation
- Deployment and Visualization
Build your portfolio with these Data Analytics Projects for Beginners
Web Scraping
Web scraping is the process of using bots to extract content and data from a website. Companies that need to harvest data use web scraping to do the same. When used ethically it makes internet information better structured for the end user. For example, search bots, crawl the websites, and rank the content, another example is a price comparison site.
All web scraping works on three fundamental principles:
- Making an HTTP request to the server
- Extracting and parsing the website code
- Saving the data locally
There are several tools that can be used for web scraping.
BeautifulSoup
BeautifulSoup is a Python package for parsing HTML and XML documents. It creates a parse tree for parsed pages that can be used to extract data from HTML, which is useful for web scraping.
Scrapy
Scrapy is a free and open-source web-crawling framework written in Python. Originally designed for web scraping, it can also be used to extract data using APIs or as a general-purpose web crawler. It is fast and can be used for data mining, monitoring and automated testing.
Pandas
Pandas is another python tool used for data manipulation and indexing. Can be used in conjunction with BeautifulSoup and uses just one language all through.
Parsehub
Parsehub is a free tool for non proficient programmers and is good to play around with for the beginners. Full features may require you to pay.
Some guidelines to keep in mind while web scraping are :
- Plan
- Python tools are popular for beginners
- Follow the legal guidelines
- Follow etiquettes like not overloading a site’s resources so much to crash it
Data Wrangling- Project for Beginners
It is the process of transforming and mapping raw data to another form which is structured, tidied and stored in a format which makes analysis easier. The raw data otherwise comes in the form of one long text which needs to be arranged in columns and rows for easier analysis. There are various processes involved in structuring data like merge two or more datasets, group and de-duplicate data, concatenate, fuzzy match, and more and these make for excellent beginner projects.
The steps of the data wrangling process include:
- Extracting the data- Identifying the relevant data and pulling it and storing it
- Carrying out EDA or Exploratory Data Analysis- Determining the structure of data and summarize its main features
- Structuring the Data- The data comes in an unstructured format and needs to be parsed which basically means extracting relevant information
- Cleaning-Algorithms are applied to clean the data using automated tools likePython and R
- Enriching- This step involves augmenting the data with data from other sources to make it richer
- Validating- Checking it for consistency, quality, and accuracy
- Publishing- Making the data accessible by depositing them into a new database or architecture
A good source of free data that you can use for your projects are:
- New York City Open Data
- Predicting Faulty Water Pumps in Tanzania
- US Climate Data etc
You can find more information here: https://www.tableau.com/learn/articles/free-public-data-sets
Exploratory data analysis Project for Beginners
Exploratory Data Analysis is how a data set’s structure is summarized and entails discovering trends within data for first useful insights. There are two types of data analysis:
- Univariate- exploring one variable at a given time
- Bivariate/Multivariate – exploring two or more variables simultaneously
They can further be divided into two kinds:
- Graphical – Visualizing the data in the form of scatter plots, box or whisker diagrams
- Non-graphical- Tables and statistics
EDA helps to explore relevant questions about the data, discover the underlying structures, look for trends and anomalies if any, test the hypothesis and identify the problems the data can solve.
Kaggle is an excellent online community platform that can provide access to data sets and GPU-integrated notebooks and help members in their data science learning journey.
Other Resources:
- Popular Python libraries for EDA: Sweetviz, dataprep.eda
- Other EDA tools: MS Excel, Trifacta
- GitHub: pandas profiling, autoviz
Sentiment Analysis Project for Beginners
Sentiment analysis is contextual mining of text (an approach to natural language processing (NLP)) to identify the emotional tone behind a body of text. This is a popular way for organizations to determine and categorize opinions about a product, service, or idea.
So students interested in linguistic data analysis can find several interesting projects. Advances in deep learning have enabled the analysis of texts to gather some useful insights regarding brand image and user’s intentions and reactions.
Sentiment Analysis can be categorized into three
- Knowledge-based techniques – Characterized by the presence of unambiguous words like happy, sad, scared or bored
- Statistical methods – They leverage elements from machine learning for semantic orientation
- Hybrid approaches – This approach leverages both machine learning and elements from knowledge representation
In the digital era where reviews, ratings, recommendations and other forms of online expression have become virtual currency and sentiment analysis a business need.
To get started you can check out these websites:
Amazon
Rotten Tomatoes
News Sites
Data Visualization Projects for Beginners
The visual representation of data makes it appealing and easy to understand. It allows one to get creative and tell a compelling story with clear action points. Tables are difficult to read and oftentimes key information gets lost, however, using tools to represent data pictorially helps present all the necessary information minus the clutter.
There are several types of data visualization
- Box and scatter plots
- Line graphs
- Pie Charts
- Histograms
- Maps
You can play around with data visualization with the help of several tools readily available online, like:
- Tableau Public
- Google Charts
- Data wrapper
- D3- Data Drive Document (JavaScript Library)
- Raw Graphs
- Grafana
Other Resources
- Python Libraries- Seaborn, matplotlib, plotly
- GitHub- Data viz tools for the web
The above projects are great for beginners and for professionals to sharpen their skills.
Data Analysis is emerging as a fast-growing field with several opportunities for some very interesting work. So don’t wait and start your Data Analysis career right away.
Learn more about FunctionUp’s upcoming data analysis cohort here.