Introduction to data analysis

Massive amounts of data are generated daily. Data analysis provides an opportunity to put these datasets to good use. This prevents them from being obsolete and redundant. Especially when they can be used to provide useful solutions to humans.

What is Data analysis?

Data analysis is the systematic process of collating, cleansing, visualizing, analyzing and interpreting data. It involves revealing hidden trends and patterns from a large dataset. These trends help businesses make informed decisions.

Data analysis is a branch of Data science.

Steps involved in data analysis

Data analysis follows a procedural pattern to achieve its goal. Below is a list of the steps involved in data analysis:

1. Define a problem:

This is the first step in data analysis. Defining and recognizing the purpose of the analysis creates a roadmap that directs data analysts. This way, every step taken would be relative to the existing problem.

For example, a bank may want to know the periods when customers deposit money most frequently.

1. Data collation and gathering:

The second step involved in data analysis is the collation and gathering of data. After defining the problem, the next step is to gather relevant data from various sources. This can be a structured dataset in a .csv file or unstructured data from other sources on the internet like social media, websites, etc.

1. Data cleaning and organization:

This process eliminates errors or inconsistencies in a dataset. It organizes data into a structured form, like tables, that would make data exploration and analysis easier. This is where duplicate data, obsolete columns or rows, missing values are removed.

1. Data exploration and visualization:

Data exploration involves finding trends in a dataset with the aid of different visualization tools. In this stage, a data analyst visualizes the data in a graph or chart and records insights from the data.

1. Data analysis/modelling:

In this step, statistical methods and machine learning algorithms are used to build predictive models. These models are trained with the dataset. They can make predictions and give answers about the dataset.

1. Interpretation of results:

Here, analysts can now make sense of the data. In this stage they can analyze outcomes, draw conclusions, validate findings and find answers to their questions. These questions are the major reason behind the exploration in the first place.

Importance of data analysis

Data analysis is used by companies to derive useful trends and insights from a dataset. The trends are used to make rational decisions. The following are the importance of data analysis:

1. Future predictions:

Data analysis is used by companies to make future predictions based on the insights and trends in the analyzed dataset.

1. Informed decisions:

Businesses use data analysis to make informed decisions about their dealings with the outside world. These informed decisions could be on customer satisfaction, expansion, recruitment of workers, etc.

1. Measure growth:

Data analysis can be used to measure a business' growth overtime. The trends from data will show how their activities have contributed to their growth or otherwise.

1. Provide new solutions:

Data analysis leads to innovative solutions or alternatives to problems faced by companies, government, individuals, healthcare, etc.

1. Employment opportunities:

With the growing reliance on data, there is an equal need for data analysts by companies, to collect, clean, analyze and make sense of the data.

Real life application of data analysis

Data analysis helps to solve real life problems. Below are some of the applications of data analysis:

1. Banking and Finance:

Data analysis is used in the banking industry to train machine learning models that detect fraudulent transactions. They also use the insights from their data history to improve customer service and make important financial decisions.

1. Cybersecurity:

Data analysis is also used in cybersecurity to detect cybercrimes like hacking.

1. Healthcare:

Data analysis is used in the healthcare sector to introduce new solutions, forecast potential disease outbreaks, etc. It can also be used to provide personalized treatments to patients based on their medical history.

1. E-commerce:

Data analysis is used in online stores like Amazon, Etsy, etc. to recommend products to users based on their search or purchase history.

1. Social media:

Data analysis is used in social media to run targeted ads based on users' interaction with posts and conversations online.

II. Who is a data analyst

A data analyst is anyone who collects, organizes, visualizes and makes sense of a dataset. They are individuals with expertise in cutting-edge technologies for analyzing data.

A. Skill-set for a data analyst

A data analyst should have the following skills in his toolbox:

1. Statistical skills:

A data analyst must have a basic knowledge in statistics and mathematics enabling them to test hypotheses and validate assumptions.

1. Coding skills:

Understanding programming languages like python or R and how to write code with them is a necessary skill for a data analyst. These languages provide libraries and frameworks that ease the data analysis process.

1. Visualization skills:

A data analyst must be able to convey insights to stakeholders and effectively understand the trends in a dataset through charts and graphs.

1. Communication skills:

A data analyst must possess effective communication skills that allows them to explain insights and trends in analyzed data to stakeholders.

What is python for data analysis?

Python is widely used in data analysis. This section reveals why you should use python for data analysis and the python libraries used for data analytics.

Why use python for data analysis?

Python is a versatile and high level programming language with an English-like syntax. It is a fast, straightforward and beginner-friendly language. Python is used in the following areas:

• Web application development.

• Automation and scripting.

• Web scraping.

• Data science and machine learning.

Python is popular for having a large community and a vast collection of libraries and frameworks. It is this robustness that makes python a go-to language for data related tasks.

It has fast and useful libraries that makes it easy to do the following:

• Collect data.

• Build and train machine learning models.

• Visualize data.

• Run scientific calculations.

• Clean, manipulate and analyze data.

B. Python Libraries for data analysis and their uses

1. NumPy

NumPy, also known as numerical python, is a python library that deals with numerical computing and mathematical calculations. It organizes data in arrays.

1. Pandas

Pandas is a powerful python library for structuring data in data frames. This data frame consists of rows and columns. Pandas help to prepare a dataset for cleaning and visualization.

1. Matplotlib

Matplotlib is a python library for efficient visualization of a dataset. It is a visualization tool for creating graphs, charts and diagrams in python.

1. TensorFlow

TensorFlow is a powerful and popular machine learning library. They are used by data analysts to build and train models.

1. SciPy

SciPy, also known as scientific Python, is a Python library used for high level computations and scientific calculations.

others include: Pytorch, seaborn, scikit-learn, scrapy, etc.

Python Vs R for data analysis

The two popular programming languages used in data analysis are Python and R. These two specialize in data related tasks.

Evaluating both using distinct criteria will help you understand how they work. The following compares Python and R on various perspectives:

1. Versatility:

Python is more versatile to R. It does not only specialize in data analytics but also supports building and training of machine learning models. This is a skill that will give data analysts an edge when applying for jobs in companies.

R on the other hand only specializes in statistical calculations, data visualization and analysis.

1. Supportive Community:

It is no news that python has a large and supportive community. Being a very popular programming language, it is easier to find support and solutions to any problem you encounter while learning this language.

Because R specializes in just one task, it's not a popular language in the tech community.

1. Customization:

When it comes to building graphs and visualizing data, R is more flexible and customizable. It gives analysts the opportunity to visualize data to their preference.

1. Personal preference:

For someone who already has a professional job but needs data analytics to improve his/her work, R would be a better choice as it only focuses on data analysis and statistics. Python is a better choice for someone who envisions a career in data analytics and needs the skills to become employable.

D. How to use python for data analysis?

There are lots of resources to kickstart your career in data analysis with python. You might need to take an introductory course in Python to understand the basics.

After that you can start a course on data science. Data science encompasses machine learning and data analysis. It is to do this, so you can have an overview of the whole process. Afterwards you can streamline your learning to data analysis alone.

Below is a list of courses to learn python and data analysis:

• Free data science certification course for beginners on simplilearn

• Free python introductory course on simplilearn

• Free python introductory course by cisco

• Free data analytics course by cisco.

Conclusion

Data Analytics is a lucrative field to begin a career in. With the increase in data generated daily, businesses need people to help them harness this data and make meaningful decisions with them.

While using Python for data analysis may be a great choice, having some basic understanding of other data analytics languages such as: R, Matlab is recommended.