DEV Community: Victor Saidi Muthoka

Introduction to Python as a Data Analytics Tool

Victor Saidi Muthoka — Thu, 03 Oct 2024 07:57:58 +0000

Introduction to Structured Query Language

Victor Saidi Muthoka — Tue, 01 Oct 2024 15:41:34 +0000

Structured query language (SQL) is a programming language for storing and processing information in a relational database. A relational database stores information in tabular form, with rows and columns representing different data attributes and the various relationships between the data values. Relational database management systems use structured query language (SQL) to store and manage data. The system stores multiple database tables that relate to each other. It is a popular query language that is frequently used in all types of applications. SQL is regularly used not only by database administrators but also by developers writing data integration scripts and data analysts looking to set up and run analytical queries. Data analysts and developers learn and use SQL because it integrates well with different programming languages. For example, they can embed SQL queries with the Java programming language to build high-performing data processing applications with major SQL database systems such as Oracle or MS SQL Server. SQL is also fairly easy to learn as it uses common English keywords in its statements. The term SQL is pronounced ess-kew-ell or sequel.

Before we dive into SQL, let’s demystify some basic terms related to database management systems;

Database: A database is a structured set of data. Imagine it as a large container that houses related information. Examples include a customer data table for a business or a bank database system for managing transactions.
Table: A table, akin to an Excel spreadsheet, is a specific structure within a database that holds related data. A table consists of rows (records) and columns (fields).
Record: A record (or row) in a table represents a single, implicitly structured data item, like a customer’s details or a single transaction in a bank.
Field: A field (or column) in a table holds a specific piece of information, like a customer’s name or transaction amount.
Primary Key: A primary key is a unique identifier for a record in a table. It ensures that each record within a table can be distinctly identified.

SQL is used for the following:

Modifying database table and index structures.
Adding, updating and deleting rows of data.
Retrieving subsets of information from within relational database management systems (RDBMSes). This information can be used for transaction processing, analytics applications and other applications that require communicating with a relational database.

SQL queries and other operations take the form of commands written as statements and are aggregated into programs that enable users to add, modify or retrieve data from database tables. A table is the most basic unit of a database and consists of rows and columns of data. A single table holds records, and each record is stored in a row of the table. Tables are the most used type of database objects or structures that hold or reference data in a relational database. Other types of database objects include the following:

Views are logical representations of data assembled from one or more database tables.
Indexes are lookup tables that help speed up database lookup functions.
Reports consist of data retrieved from one or more tables, usually a subset of that data that is selected based on search criteria.

Each column in a table corresponds to a category of data -- for example, customer name or address -- while each row contains a data value for the intersecting column.

SQL implementation involves a server machine that processes the database queries and returns the results. The SQL process goes through several software components, including the following:

Parser
The parser starts by tokenizing, or replacing, some of the words in the SQL statement with special symbols. It then checks the statement for the following;
Correctness
The parser verifies that the SQL statement conforms to SQL semantics, or rules, that ensure the correctness of the query statement. For example, the parser checks if the SQL command ends with a semi-colon. If the semi-colon is missing, the parser returns an error.
Authorization
The parser also validates that the user running the query has the necessary authorization to manipulate the respective data. For example, only admin users might have the right to delete data.
Relational engine
The relational engine, or query processor, creates a plan for retrieving, writing, or updating the corresponding data in the most effective manner. It checks for similar queries, reuses previous data manipulation methods, or creates a new one. It writes the plan in an intermediate-level representation of the SQL statement called byte code. Relational databases use byte code to efficiently perform database searches and modifications.
Storage engine
The storage engine, or database engine, is the software component that processes the byte code and runs the intended SQL statement. It reads and stores the data in the database files on physical disk storage. Upon completion, the storage engine returns the result to the requesting application.

SQL commands
Structured query language (SQL) commands are specific keywords or SQL statements that developers use to manipulate the data stored in a relational database. You can categorize SQL commands as follows;
Data definition language (DDL) - refers to SQL commands that design the database structure. Database engineers use DDL to create and modify database objects based on the business requirements. For example, the database engineer uses the CREATE command to create database objects such as tables, views, and indexes.
Data query language (DQL) - consists of instructions for retrieving data stored in relational databases. Software applications use the SELECT command to filter and return specific results from a SQL table.
Data manipulation language (DML) - DML statements write new information or modify existing records in a relational database. For example, an application uses the INSERT command to store a new record in the database.
Data control language - Database administrators use data control language (DCL) to manage or authorize database access for other users. For example, they can use the GRANT command to permit certain applications to manipulate one or more tables.
Transaction control language - The relational engine uses transaction control language (TCL) to automatically make database changes. For example, the database uses the ROLLBACK command to undo an erroneous transaction.

SQL is a great language to learn because it's the primary database language used for data processing tasks and is used across various industries. The following reasons highlight the importance of SQL;

Backbone of the data industry. SQL is considered the backbone of the data industry. It's widely used by data-centric professionals including data analysts, data scientists, business analysts and database developers.
Universal language. SQL is a universal language that is transferable to other disciplines and languages. Learning SQL can help one understand the workings of other languages such as Python and Java. It also makes collaboration easy, as it has a large supportive community.
In-demand skill. SQL knowledge is one of the most in-demand skills in the field of data science. It appears in a significant percentage of data science job postings, making it a prized skill for professionals in this field.
Data manipulation. SQL is well-suited for data manipulation. It enables users to easily test and manipulate data, making it efficient for tasks such as filtering, sorting and aggregating data.
Rapid query processing. SQL enables rapid query processing, enabling users to retrieve, manipulate or store data quickly and efficiently. However, optimizing queries for rapid processing involves a combination of proper indexing, query optimization and database design considerations.
Security features. SQL provides various security features such as authentication, access control, audit trails and encryption, making it easy to manage permissions and ensure the security of data.
Commonality and compatibility. SQL is widely used in various IT systems and is compatible with multiple other languages. Its commonality benefits beginners in the profession, as they are likely to use SQL throughout their careers. It also contributes to ease of application and improves the production and efficiency of businesses.
Scalability. SQL is suitable for organizations of any size. It can easily scale up to accommodate future growth, making it a versatile choice for small and large businesses alike.
Open source and community support. SQL is an open source language with a vibrant community of developers that regularly provide updates and troubleshooting assistance to SQL users.
Cost-effective. Due to its open source nature, SQL is more cost-effective than proprietary solutions, making it ideal for organizations with budget constraints.

Commonly used SQL commands
Most SQL commands are used with operators to modify or reduce the scope of data operated on by the statement. Some commonly used SQL commands are;
SQL SELECT. The SELECT command is used to get some or all data in a table. SELECT can be used with operators to narrow down the amount of data selected.
SQL CREATE. The CREATE command is used to create a new SQL database or SQL table. Most versions of SQL create a new database by creating a new directory, in which tables and other database objects are stored as files.
The CREATE DATABASE statement creates a new SQL database while the CREATE TABLE command is used to create a table in SQL.
SQL DELETE. The DELETE command removes rows from a named table.
SQL INSERT INTO. The INSERT INTO command is used to add records into a database table.
SQL UPDATE. The UPDATE command is used to make changes to rows or records in a specific table.
SQL statements can use loops, variables and other components of a programming language to update records based on different criteria.

To ensure optimal performance and ease of use, follow these SQL best practices;

Naming Conventions: Use clear, concise names for tables and columns. The names should describe the data they hold.
Data Types: Be specific about your data types. If a numeric column will only hold integers, specify it as such.
Indexing: Use indexing wisely to speed up data retrieval. However, remember that while indexes speed up data retrieval, they can slow down data input.
Backing up Data: Always backup your data regularly to prevent data loss in case of any failure or corruption.

SQL’s relational engine and storage engine work hand in hand to ensure efficient data management and retrieval. These elements, combined with SQL’s versatility across operating systems and database software, make SQL a crucial skill for aspiring database administrators and software engineers alike.

SQL, or Structured Query Language, is a standard language for managing and manipulating relational databases. Understanding SQL basics and syntax, mastering database management with SQL, and adhering to SQL best practices are foundational skills for anyone dealing with data.

The Ultimate Guide to Data Analytics(Data Engineering).

Victor Saidi Muthoka — Sun, 25 Aug 2024 14:27:07 +0000

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It encompasses the creation of data pipelines to ensure data flows efficiently from source systems to data storage and analytics platforms. Data engineers extract data from various sources, transform it into a usable format, and load it into data storage solutions like data warehouses or data lakes. Organizations and industries have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts. Organizing requires data engineers to put together the structure in the warehouse so that data can be accessed when getting queried. Cleaning requires data engineers to remove duplicates, monitor ingestion, and make sure that the data presented in the right format.

Data engineering comprises several key components that work in synergy to facilitate the extraction, transformation, and storage of data. These components include;

Data Sources Data engineers work with various sources, such as databases, web services, APIs, and IoT devices, to collect and ingest raw data. They need to understand the structure and format of each data source to ensure seamless integration into the data pipeline.
Data Integration It involves combining data from multiple sources, such as different databases or sources with varying formats, to create a unified view. Data engineers use techniques like data extraction, transformation, and loading (ETL) to merge and transform data into a consistent format.
Data Modeling Data engineers create data models that define the structure, relationships, and constraints of the data to be stored and processed. These models serve as blueprints for organizing and optimizing data storage, ensuring efficient data retrieval and analysis.
Data Quality Ensuring data accuracy, consistency, and completeness is critical for reliable analysis. Data engineers implement processes to validate and cleanse the data to improve its quality. They use techniques like data profiling, data cleansing, and data deduplication to identify and resolve data quality issues.
Data Governance Data governance focuses on establishing policies, processes, and controls to ensure data compliance, security, and privacy. Data engineers collaborate with legal and compliance teams to define data governance frameworks and implement measures to protect sensitive data.

Data engineering is a fundamental discipline that underpins the success of data-driven organizations. By designing and constructing robust data infrastructure, data engineers enable the efficient capture, storage, processing, and delivery of data. They play a crucial role in ensuring data quality, integration, and governance, paving the way for valuable insights and innovations.

With the right set of skills and knowledge, you can launch or advance a rewarding career in data engineering. By earning a degree, you can build a foundation of knowledge you’ll need in this quickly evolving field. Besides earning a degree, there are several other steps you can take to set yourself up for success like;

Develop your data engineering skills. Learn the fundamentals of cloud computing, coding skills, and database design as a starting point for a career in data engineering.

Proficiency in coding languages is essential to this role, so take courses to learn and practice your skills in common programming languages include SQL, Python, Java, R, and Scala.
Databases rank among the most common solutions for data storage. You should be familiar with both relational and non-relational databases, and how they work.
ETL(Extract Transform and Load) is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.
Data storage as not all types of data should be stored the same way, especially when it comes to big data. As you design data solutions for a company, you’ll want to know when to use a data lake versus a data warehouse.
Automation is a necessary part of working with big data simply because organizations are able to collect so much information. You should be able to write scripts to automate repetitive tasks.
Data engineers don’t just work with regular data. They’re often tasked with managing big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka. Also, while machine learning is more the concern of data scientists, it can be helpful to have a grasp of the basic concepts to better understand the needs of data scientists on your team.
Data engineers don’t just work with regular data. They’re often tasked with managing big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka. You’ll also need to understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services. Beginners may consider a course in Amazon Web Services (AWS) or Google Cloud.
While some companies might have dedicated data security teams, many data engineers are still tasked with securely managing and storing data to protect it from loss or theft.

Certification
A certification can validate your skills to potential employers, and preparing for a certification exam is an excellent way to develop your skills and knowledge. Options include the Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, or Google Cloud Certified Professional Data Engineer.
Building a portfolio and projects
A portfolio is a key component in a job search, as it shows recruiters, hiring managers, and potential employers what you can do. You can add data engineering projects you've completed independently or as part of coursework to a portfolio website. Alternately, post your work to the Projects section of your LinkedIn profile or to a site like GitHub—both free alternatives to a standalone portfolio site.
Start off in entry-level roles
Many data engineers start off in entry-level roles, such as business intelligence analyst or database administrator. As you gain experience, you can pick up new skills and qualify for more advanced roles.

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance. Some of their roles are;

Designing and implementing data pipelines to extract, transform, and load data from various sources. This involves understanding the data sources, identifying the relevant data, and creating efficient processes to extract and transform the data.
Optimizing data storage systems for efficient access and retrieval. Data engineers work on designing and implementing databases and data storage solutions that can handle large volumes of data and provide fast access to it.
Develop algorithms to transform data into useful, actionable information while creating new data validation methods and data analysis tools
Building and maintaining data warehouses, data lakes, or data marts. These are essential components of a data infrastructure that allow for efficient storage and organization of data.
Collaborating with data scientists, analysts, and other stakeholders to understand their data requirements. Data engineers work closely with other members of the data team to ensure that the infrastructure meets the needs of the organization.
Ensuring data security, integrity, and compliance with regulations. Data engineers are responsible for implementing security measures to protect the data and ensuring that it is stored and processed in accordance with legal and regulatory requirements.

A good data engineer will anticipate data scientists’ questions and how they might want to present data. Data engineers ensure that the most pertinent data is reliable, transformed, and ready to use. This is a difficult feat, as most organizations rarely gather clean raw data. While data engineers may not be directly involved in data analysis, they must have a baseline understanding of company data to set up appropriate architecture. Creating the best system architecture depends on a data engineer’s ability to shape and maintain data pipelines. Experienced data engineers might blend multiple big data processing technologies to meet a company’s overarching data needs.

Cloud computing has revolutionized the field of data engineering by providing readily available infrastructure and scalable resources. Cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud offer services that simplify the deployment and management of data engineering workflows. Data engineers can leverage cloud storage, serverless computing, and managed data processing services to build efficient and cost-effective data pipelines.

The future of data engineering is promising due to the increasing importance of big data, AI, and machine learning. The demand for skilled data engineers will continue to rise with the growth of data volumes. Technologies like cloud computing, real-time data processing, and advanced analytics will further expand opportunities in this field.

Understanding Your Data: The Essentials of Exploratory Data Analysis

Victor Saidi Muthoka — Sun, 11 Aug 2024 11:14:12 +0000

Exploratory Data Analysis (EDA) is used by data scientists to examine and visualize data to understand its main characteristics, identify patterns, spot anomalies, and test hypotheses. It helps summarize the data and uncover insights before applying more advanced data analysis techniques. It is a data analytics process that aims to understand the data in depth and learn its different characteristics, often using visual means. This allows one to get a better feel for the data and find useful patterns.
It is used to ensure the results they produce are valid and applicable to any desired business outcomes and goals and also helps stakeholder by confirming they are asking the right questions. Furthermore, it allows the identification of data quality issues, such as missing values or errors, which can be addressed before proceeding to more advanced analysis. This preliminary analysis enhances the reliability and accuracy of the subsequent modeling and ensures that the insights derived are valid and actionable. EDA allows data scientists to make informed decisions and derive meaningful insights that drive business strategies and solutions.

Specific statistical functions and techniques you can perform with EDA tools include:

Clustering and dimension reduction techniques, which help create graphical displays of high-dimensional data containing many variables. It reduces the number of variables under consideration to simplify models, reduce computation time, and mitigate the curse of dimensionality. It uses techniques like; Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Linear Discriminant Analysis (LDA) etc.
Univariate visualization of each field in the raw dataset, with summary statistics. It focuses on analyzing a single variable at a time to understand the variable's distribution, central tendency, and spread. It uses techniques like; Descriptive statistics (mean, median, mode, variance, standard deviation), Visualizations (histograms, box plots, bar charts, pie charts) etc.
Bivariate visualizations and summary statistics that allow you to assess the relationship between each variable in the dataset and the target variable you’re looking at. It examines the relationship between two variables to understand how one variable affects or is associated with another. It uses techniques like; Scatter plots, Correlation coefficients (Pearson, Spearman), Cross-tabulations and contingency tables, Visualizations (line plots, scatter plots, pair plots) etc.
Multivariate visualizations, for mapping and understanding interactions between different fields in the data. It investigates interactions between three or more variables to understand the complex relationships and interactions in the data. It uses techniques like; Multivariate plots (pair plots, parallel coordinates plots), Dimensionality reduction techniques (PCA, t-SNE), Cluster analysis, Heatmaps and correlation matrices etc.
Descriptive Statistics. Summarizes the main features of a data set to provide a quick overview of the data. It uses techniques like; Measures of central tendency (mean, median, mode), Measures of dispersion (range, variance, standard deviation), Frequency distributions etc.
Graphical Analysis. It uses visual tools to explore data to identify patterns, trends, and data anomalies through visualization. It uses techniques like; Charts (bar charts, histograms, pie charts), Plots (scatter plots, line plots, box plots), Advanced visualizations (heatmaps, violin plots, pair plots) etc.

Using the following tools for exploratory data analysis, data scientists can effectively gain deeper insights and prepare data for advanced analytics and modeling:

Python Libraries
Pandas: Provides data structures and functions needed to manipulate structured data seamlessly. Used for Data cleaning, manipulation, and summary statistics.
NumPy: Supports large, multi-dimensional arrays and matrices and a collection of mathematical functions. Used for numerical computations and data manipulation.
Matplotlib: A plotting library that produces static, animated, and interactive visualizations. Used for basic plots like line charts, scatter plots, and bar charts.
Seaborn: Built on Matplotlib, it provides a high-level interface for drawing attractive statistical graphics. Used for advanced visualizations like heatmaps, violin plots, and pair plots.
SciPy: Builds on NumPy and provides many higher-level scientific algorithms. Used for statistical analysis and additional mathematical functions.
Plotly: A graphing library that makes interactive, publication-quality graphs online. Used for Interactive and dynamic visualizations.
R Libraries
ggplot2: A framework for creating graphics using the principles of the Grammar of Graphics. Used for complex and multi-layered visualizations.
dplyr: A set of tools for data manipulation, offering consistent verbs to address common data manipulation tasks. Used for data wrangling and manipulation.
tidyr: Provides functions to help you organize your data in a tidy way. Used for data cleaning and tidying.
shiny: An R package that makes building interactive web apps straight from R easy. Used for Interactive data analysis applications.
plotly: Also available in R for creating interactive visualizations. Used for Interactive visualizations.
Integrated Development Environments (IDEs)
Jupyter Notebook: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. Used for combining code execution, rich text, and visualizations.
RStudio: An integrated development environment for R that offers tools for writing and debugging code, building software, and analyzing data. Used for R development and analysis.
Data Visualization Tools
Tableau: A top data visualization tool that facilitates the creation of diverse charts and dashboards. It is used for Interactive and shareable dashboards.
Power BI: A Microsoft business analytics service offering interactive visualizations and business intelligence features. It is used for Interactive reports and dashboards.
Statistical Analysis Tools
SPSS: A comprehensive statistics package from IBM. It is used in Complex statistical data analysis.
SAS: A software suite developed by SAS Institute for advanced analytics, business intelligence, data management, and predictive analytics. It is used for Statistical analysis and data management.
Data Cleaning Tools
OpenRefine: A powerful tool for cleaning messy data, transforming formats, and enhancing it with web services and external data. Used for Data cleaning and transformation.
SQL Databases: Tools like MySQL, PostgreSQL, and SQLite are used to manage and query relational databases for data extraction, transformation, and basic analysis.

Steps for Performing Exploratory Data Analysis

Performing Exploratory Data Analysis (EDA) involves a series of steps designed to help you understand the data you’re working with, uncover underlying patterns, identify anomalies, test hypotheses, and ensure the data is clean and suitable for further analysis.

Understand the Problem and the Data
By thoroughly knowing the problem and the information, you can better formulate your evaluation technique and avoid making incorrect assumptions or drawing misguided conclusions. It is also vital to contain situations and remember specialists or stakeholders to this degree to ensure you have complete know-how of the context and requirements.
Import and Inspect the Data
Import the data into your evaluation environment to gain initial know-how of its structure, variable kinds, and capability issues. Examine the size of the facts (variety of rows and columns) to experience its length and complexity. Check for missing values and their distribution across variables, as missing information can notably affect the quality and reliability of your evaluation. Identify facts sorts and formats for each variable, as these records may be necessary for the following facts manipulation and evaluation steps.Look for any apparent errors or inconsistencies in the information, such as invalid values, mismatched units, or outliers, that can indicate exceptional issues with information.
Handle Missing Data
Missing records can significantly impact the quality and reliability of your evaluation. It’s critical to pick out and deal with lacking information as it should be, as ignoring or mishandling lacking data can result in biased or misleading outcomes.
Some techniques you could use to handle missing statistics are like:
Understanding the underlying mechanisms can inform the proper method for handling missing information.
Decide whether to eliminate observations with lacking values (listwise deletion) or attribute (fill in) missing values.
Use suitable imputation strategies.
Even after imputation, lacking facts can introduce uncertainty and bias. It is important to acknowledge those limitations and interpret your outcomes with warning.
Handling missing information nicely can improve the accuracy and reliability of your evaluation and save you biased or deceptive conclusions. It is likewise vital to record the techniques used to address missing facts and the motive in the back of your selections.
Explore Data Characteristics
It entails examining your variables’ distribution, crucial tendency, and variability and identifying any ability outliers or anomalies. Understanding the characteristics of your information is critical in deciding on appropriate analytical techniques, figuring out capability information first-rate troubles, and gaining insights that may tell subsequent evaluation and modeling decisions. Calculate summary facts (suggest, median, mode, preferred deviation, skewness, kurtosis, and many others.) for numerical variables: These facts provide a concise assessment of the distribution and critical tendency of each variable, aiding in the identification of ability issues or deviations from expected patterns.
Perform Data Transformation
Data transformation is a critical step within the EDA process because it enables you to prepare your statistics for similar evaluation and modeling. Depending on the traits of your information and the necessities of your analysis, you may need to carry out various ameliorations to ensure that your records are in the most appropriate layout.
Visualize Data Relationships
Visualization is an effective tool in the EDA manner, as it allows to discover relationships between variables and become aware of styles or trends that may not immediately be apparent from summary statistics or numerical outputs. To visualize data relationships, explore univariate, bivariate, and multivariate analysis.
Handling Outliers
These are data item/object that deviates significantly from the rest of the (so-called normal)objects. They can be caused by measurement or execution errors. The analysis for outlier detection is referred to as outlier mining. There are many ways to detect outliers, and the removal process of these outliers from the dataframe is the same as removing a data item from the panda’s dataframe.Identify and inspect capability outliers through the usage of strategies like the interquartile range (IQR), Z-scores, or area-specific regulations: Outliers can considerably impact the results of statistical analyses and gadget studying fashions, so it’s essential to perceive and take care of them as it should be.
Communicate Findings and Insights
You now effectively discuss your findings and insights. This includes summarizing your evaluation, highlighting fundamental discoveries, and imparting your outcomes cleanly and compellingly.

Exploratory Data Analysis forms the bedrock of data science endeavors, offering invaluable insights into dataset nuances and paving the path for informed decision-making. By delving into data distributions, relationships, and anomalies, EDA empowers data scientists to unravel hidden truths and steer projects toward success.

Expert advice on how to build a successful career in data science, including tips on education, skills and job searching.

Victor Saidi Muthoka — Sun, 04 Aug 2024 13:38:06 +0000

Data science is a discipline that combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. The insights can be used to guide decision making and strategic planning. Data science is one of the fastest growing field across every industry since there's an accelerating volume of data sources, and subsequently data. Organizations are increasingly reliant on data scientist to interpret data and provide actionable recommendations to improve business outcomes.

The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. A project typically has the following stages;

Data collection of both raw structured and unstructured data from all relevant sources using a variety of methods like manual entry, web scraping, and real-time streaming data from systems and devices.
Data storage and processing based on the type of data that needs to be captured. Standards should be set around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. There's also cleaning data, deduplicating, transforming and combining the data using integration technologies.
Data analysis. Where the data scientist examines biases, patterns, ranges, and distributions of values within the data. It also allows data analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning.
Communication. The insights are presented as reports and other data visualizations that make the insights easier for business analysts and other decision-makers to understand.

If you aspire to pursue a data science career, you should develop proficiency in programming languages like Python, Java, R, and SQL/MySQL, and strengthen your foundation in Applied Mathematics and Statistics. Exposure to the field early on can help determine your interest and fit. Ideal subjects for study include Computer Science, Information Technology, Mathematics, Statistics, and Data Science. Key skills include data science, machine learning, Python, R, research, SQL, data analysis, analytical skills, teamwork, and communication.

After gaining one to three years of work experience, you can progress to Senior Data Scientist or specialize in Machine Learning and AI Engineering. Certification becomes valuable at this stage, with organizations favoring certified data scientists. Consider earning relevant data science certifications to enhance your credentials. As a Senior Data Scientist, your responsibilities include building well-architected products, revisiting high-performing systems, and mentoring junior associates. AI/Machine Learning Engineers focus on designing, creating, and deploying end-to-end machine learning solutions. Skills required encompass Artificial Intelligence, Deep Learning, Machine Learning, Natural Language Processing, Data Science, Python, C++, SQL, Java, and software engineering. Then you can level up to Principal Data Scientist whose highly experienced and well-versed in data science models. They work on high-impact business projects, possess a Ph.D., and often hold a principal data scientist certification. Their role involves understanding challenges in multiple business domains, discovering new opportunities, and demonstrating leadership excellence in data science methodologies. There's also the Data Science Manager/Architect who has a combination of knowledge of database systems and programming languages. Responsibilities include team leadership, setting priorities, and communicating findings to management.

Some of the places you can hunt for data science jobs are like job sections of sites like Indeed, LinkedIn, Glassdoor. Also some well-liked data-science-specific job boards are like; Kaggle Jobs, Outer Join, KDNuggets Jobs, Data Science Central - Analytic Talent among others. You can also register on different platforms and advertise your services to companies and individuals who are looking for someone to fulfill a one-time or short-term data science task as a freelancer. Social media can be another great way to interact with members of the data science community and make connections. Despite all the hype, data science is still a pretty small field, and even the most influential accounts tend to be backed by down-to-earth folks who are happy to discuss data science topics and offer help and advice to anyone who asks. Twitter and Quora, in particular, are networks with a pretty active data science presence and easy, direct interaction. Many job applicants only ever look at online job boards because it’s convenient. But if you’re looking to boost your chances of success, you may be better off taking a much more personal approach by networking with people from the tech field more. Data science and tech industry meetups and events can be a great way to connect with other people in the community, network, and find jobs.

Data science has emerged as a revolutionary field that is crucial in generating insights from data and transforming businesses. It's not an overstatement to say that data science is the backbone of modern industries.

Navigating the data scientist career path is challenging yet rewarding. As you progress, continuously evaluate and enhance your skills, daring to make data work for you and your organization. According to Glassdoor and Forbes, demand for data scientists will increase by 28 percent by 2026, which speaks of the profession’s durability and longevity, so if you want a secure career, data science offers you that chance. So, if you’re looking for an exciting career that offers stability and generous compensation, then look no further!