DEV Community: Sheila

"The Ultimate Guide to Data Analytics."

Sheila — Fri, 30 Aug 2024 14:53:47 +0000

Introduction

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Data analytics is a carefully curated process to convert and evaluate raw data into practical solutions that optimize the performance of companies and decision making by creating a storytelling process with data. Data analytics mainly comprises five major steps: data collection, data storage, data processing, data cleansing, and data analysis.

Data collection processes

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Once the objectives and aims of the analysis is set, a data analyst has to identify and collect data from reputable sources. It is therefore crucial that data collected is an accurate representation of the facts. This process can be categorized into ELT or ETL.

Extract Transform Load (ETL)
In ETL, the first step is to extract data from files, databases, APIs, etc., then this data undergoes transformation processes by duplicate removal, identifying missing data, dropping null values, aggregates, etc. After transformation, data is loaded into a database or a warehouse for storage.

Extract Load Transform (ELT)
In ELT, the first step is to extract data similar to the ETL process, and then the data collected is loaded into a data repository and thereafter transformed.

Some of the python libraries that are crucial in these two processes include Pandas, NumPy, Apache Airflow and SQL Alchemy. These two approaches have different benefits in data analytics. ETL ensures high quality data therefore promotes flexibility and agility of data. It also ensures that security concerns are addressed especially where data is of sensitive nature. ETL therefore allows for masking of data and provides the option to include encryption keys. Where the data volume is extremely big such as in the case of big data, ELT will be a good option because it ensures that data loss does not occur and provides analysts with the option of extracting data they may need from a larger dataset.
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Data analysis and visualization Types

After data is processed, organized and securely stored, this data is utilized to interpret and visualize this information. Data analytics can be categorized into four types: descriptive, prescriptive, predictive, or diagnostic analytics.

*Descriptive Analytics
This provides a summary of what and why occurrences are there. It is used to identify trends.

Prescriptive Analytics
Provides recommendations by using machine learning and industry knowledge to give solutions on steps to be taken.

Predictive Analytics
This is an analytic type that forecasts and predicts what may or may not happen in future. It often involves the application of machine learning.

Diagnostic Analytics
This analytic type focuses on diagnosis / analyzing the why. Analysts do this to understand why patterns and trends are the way they are by analyzing previous patterns from past events.

Data Analytics Techniques

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There are many different analytics techniques. These are some of the techniques often implemented in analytics.

Regression analysis
cluster analysis
Time series analysis
Classification analysis
Text Analysis (NLP- Natural language processing))
Principal component analysis
Descriptive statistics
Inferential statistics

Fundamental Tools for Data Analytics

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These are some of the crucial tools that are necessary for every data analyst.
1. Microsoft Excel
This is a tool that is used to clean and transform data using formulation as well as visualization.
2. Python
This programming language is a staple with libraries such as NumPy, pandas, Matplotlib, seaborn, Sklearn and beautiful soup. All these libraries have different functions that are important in numerical analysis and data manipulation.

R This is another programming language that is specifically designed to conduct statistical computations. 4. Tableau and power BI These are two popular visualization software that create collaborative dashboards to visualize data and indicate patterns. 5. Structured Query Language (SQL) This tool is essential in examining and managing relational databases. It is useful in extracting, filtering and joining tables. Other SQL tools include NoSQL, which also stores and retrieves data in a more flexible way. 6. Jupyter Notebooks This is a free open-source web application that allows for creation and sharing of documents containing live codes. It can be run on any browser or desktop and allows for the creation of reports from them. **

Conclusion

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It is crucial to understand that the choice of tools to be used in data analytics is subjective and dependent on the intended outcome. Data analytics is a wide field, and any data analyst, data scientist, or machine learning expert should familiarize themselves with these tools and many more in order to enhance their productivity and increase efficiency in the data driven ecosystem.

Understanding Your Data: The Essentials of Exploratory Data Analysis.

Sheila — Tue, 20 Aug 2024 11:43:22 +0000

## Introduction****
Exploratory Data analysis is one of the most crucial process in ascertaining that the data analysis process is seamless to prevent data bias and false conclusions due to inconsistencies. It aids in exploring data structure, point out anomalies and check for assumptions in order to improve data quality, especially because there are many. These essentials include the following:

Data collection

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This is the first step that every data analyst, data engineer and data scientist should be conversant with. You have to be cognizant of where your data will come from, what type of data you will need, how you will gather the data, etc. Some of the data collection tools often employed by data analysts in this process includes manual data entry from interviews, questionaries and surveys, observation, focus group data and consumer data, either manually or by use of survey platforms such as survey monkey, google forms and questionnaire Pro, which can then be stored in databases and later on conducted by using software such as SQL AND NoSQL.
Data can also be collected by using data integration tools such as Apache Nifi and Talend, which can support scalable data routing and transformation. Web scrapping tools such as BeautifulSoup and scrappy are used to collect data from websites. These are python libraries that are able to pull data out of websites that are static or dynamic without a lot of struggle.
API tools such as postman and rapid API offer immense support in testing APIs that have been tasked to collect data from web services and provide access to many other APIs worldwide. Other tools for the collection of data include data collection apps such as Open Data Kit (ODK) and Kobo Toolbox, both of which are used by organizations to collect, manage, and use data in challenging environments.
To ensure that you have collected good data, ensure that you are aware of data sources and databases that provide data that is accurate, without bias and well organized. Data can be collected in different forms, such as Csv files, excel files, Pdf files or from websites that may be static or dynamic, among many other forms.
Data collection is mostly subjective to the specific field of interest and organization, which means that there are many ways to collect data depending on the tools used in different professions. Since different fields of specificity have different ways in which data should be collected, ethical considerations should be highly considered. For example, data from the medical field can be collected through testing of people’s health status, which comes with its own set of ethical challenges, whereas data about climate or geography can be collected by using QGIS, etc. It is therefore crucial to understand that good data collection follows a defined set of objectives and aims in order to ensure that a dataset is accurate and caters to your specific need.
Data Cleaning and rangling
After the dataset is identified and collected, it is crucial to undertake rigorous data structuring. Data should be formatted to the required format to make it easier to work with. For example, data can be in form of a pdf file, which can be transformed into a csv file where needed in order to facilitate organization of said data into tables, charts, etc.
Next, data cleaning is conducted to ensure that the quality of your data is impeccable and can be used to come up with concise conclusions and recommendations to the end user. Before any data is utilized, a data analyst should check for any cases of inconsistencies, missing/null entries duplicate entries if present. Missing values or double entries oftentimes lead to false outcomes, which may impact crucial decision-making. Therefore, anyone analyzing data should ensure that their dataset is accurate.
After cleaning, an analyst should enrich the dataset by including additional information lacking in the dataset, merging with other datasets to get a bigger scope, or even conducting feature engineering in order to provide other variables that can improve the analysis process. After all these, an analyst should check for accuracy and consistency once more to ascertain the quality of data before moving forward.

Descriptive statistics

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After preparation of the dataset, data characteristics have to be outlined. This gives a brief summary of the overall dataset by indicating the mean, median, mode, and standard deviation, as well as the percentiles of data, distribution analysis to check for the central measures of tendency, distribution shapes, i.e., skewness and kurtosis, and visualization techniques to check for the distribution of dataset and probability density.

Data visualization

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This stage of EDA encompasses several steps, which include the identification of data variables. Data variables can be classified into the following.

Univariate analysis: visualizing individual variables
Bivariate analysis: visualizing the relationships between two variables by using tools such as scatter plots, correlation matrices, etc.
Multivariate analysis: visualizing data by analyzing relationships between more than two sets of data or multiple variables.
Outliers’ identification: the identification of data that is unusual or varies significantly from the overall dataset within specific variables.
Hypotheses testing and formulation: The testing of evidence to reject or accept a null or alternative hypothesis.
Testing assumptions: checking whether the data conforms to previously mentioned assumptions.

Communication of findings

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This is the final step in the EDA process. A summary of the evaluation process is conducted, and findings are mentioned. The context of the data is articulated, and the scope and objectives of the analysis are identified. In this final stage, patterns, anomalies and perceptions should be discussed and suggestions are made for future areas to improve on.

Conclusion

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To conclude, exploratory data analysis is a formidable process that enables an in-depth understanding of data and datasets by use of scientific statistical analysis techniques in order to enable fact driven decision making. As such, this process should be approached with meticulous precision in order to avoid inaccuracies.

Data Analysis, The Ultimate Guide to Data Analytics: Techniques and Tools

Sheila — Sun, 04 Aug 2024 20:37:47 +0000

What is Data Analysis?

Data analysis is the process of analyzing, processing, and analyzing these data sets from many different data sources in order to interpret and come up with conclusions and recommendations. The increase in data types and sources has created a complex data ecosystem, thus the need for revolutionary data analysis tools. This article will delve into some of the necessary tools and techniques that are crucial in tackling data queries and challenges.

Crucial tools:

To start with, the first crucial basic tool that should be a staple for any data analyst is to have a basic understanding of how technical programs work. One of the juggernaut that is Python. Python is a programming language used for writing code and scraping data from databases and websites. Secondly, a data analyst should have access to text editors like VSCode and scriptwriting and database programs such as SQL and DBeaver and have a proper understanding of data storage software such as Microsoft Excel and data presentation software such as Power BI. All these software's and tools enable ease of processing data in any data analyst's career.
As I continue with my learning experience, I have also come across several analytical techniques that I still need to further delve to in order to be proficient in this field. Some of these skills include:
i. Descriptive analysis
ii. Cluster analysis
iii. Regression analysis
iv. predictive analysis
All these skills paired well with critical thinking and base knowledge in the field of research makes a good data analyst.

Conclusion

To conclude, data analysis is the door that opens opportunities for solving real life problems. It is therefore crucial for data analysts to have high expertise in the tools used in this field and be flexible to adapt to any changes within this fast-evolving industry.