DEV Community: Dennis Njenga

Complete Roadmap for Beginners in Data Science 2023 - 2024.

Dennis Njenga — Sun, 01 Oct 2023 20:06:48 +0000

In a world where data is generated every second, rewarding careers have emerged. A data scientist is a sort-after career today. This is because industries are trying to make the best data-driven decisions to maximize their profits in the most efficient way possible without compromising on quality

Data science is a future-proof career to some extent if you think about it, a world that is interconnected through technology, loads of data is generated every second. Companies are at the forefront of using this data to make sense of customer behavior, improve the quality of their products, and generally improve their operations and sales.

Table of content

a. What is Data science?
b. What does a Data scientist do?
c. What skills are needed to be a data scientist?
d. What tools are used by a data scientist?
e. Data scientist project Life cycle
f. Difference between a data scientist and other data fields
g. Conclusion

Who is a Data Scientist?

Data science is a multi-disciplinary field of study that involves big data. Companies are increasingly incorporating it into their operations to improve efficiency, retain quality, and improve their fortunes. It involves creating new innovative ways of modeling and understanding the unknown from raw data.
Data science helps in making data-driven decisions by studying massive amounts of data to extract meaningful insights.

What does a Data scientist do?

A Data scientist helps bring to understanding what volumes of data mean and how the business can use this information to their advantage in decision making. To achieve this, a data scientist works with various stakeholders in the business.

A data scientist extracts data from various sources, stores this data, prepares the data, and analyzes it using machine learning models to give meaningful insights.

Skills Required to be a Data Scientist

Data scientists are required to be very curious first and foremost. This is because they deal with massive amounts of data. This allows them to create models that can answer the right questions as far as the data is concerned. Other skills needed to be a data scientist are;

Communication skills,
Mathematics and Statistics,
Visualization,
Deep learning,
Data Wrangling, and
Data warehousing

Tools Used by a Data Scientist

Data scientists are mostly required to understand Python and R programming languages. Python helps create lightweight algorithms used in data manipulation. In-built frameworks like Matplotlib, Pandas, Seaborn, Keras, Scikit-learn, Tensorflow, Pytorch, and Beautifulsoup make it easy to extract insights from data.
Other tools used by Data scientists are visualization tools like PowerBI, Tableau, and Excel. In addition, some data scientists may be involved in data warehousing projects where structured and unstructured data is stored. This therefore requires the data scientist to understand SQL and NoSQL.

Data Science Project Life cycle

Using the skills highlighted and the tools available, a data scientist can carry out various projects. These projects are expected to follow certain guidelines to make meaningful insights into the data. These steps are;

Business Operation understanding – for a project to make sense to stakeholders, the scientist works with all the business stakeholders to understand the operations of the business. These help in understanding where Data will come from as well.
Data mining – involves collecting data from various sources such as the companies’ databases, and web scraping from websites.
Data cleansing – involves eliminating inconsistencies in data to ensure accuracy in the results
Data exploration – Data is analyzed at this stage to ensure it answers the business questions intended for the project.
Predictive modeling – involves training machine learning models, performance evaluation, and using them to make predictions.
Data visualization – Findings are then communicated to stakeholders.

Difference Between Data Science and other data fields

Data science shares similarities with various data fields. Let us break down these fields and the various roles they serve.

_Data Analyst _ Involved in extracting and reporting insights from data for businesses to make informed decisions.
Data Engineer Is involved in designing, building, and managing data infrastructure, creating data pipelines, and ensuring optimal performance.
Data Architect They are involved in designing the overall structure and organizing data within a business. Data model creation and definition of data standards are among some of their roles.

Conclusion

Data Science is a critical aspect of business operations in these times where data is the new "oil". Data scientists can work in various industries like medicine, finance, and manufacturing. This therefore means that demand for data science professionals can only grow with time.

Fundamental Tips for Python Beginners

Dennis Njenga — Wed, 28 Jun 2023 02:26:02 +0000

Python is a very powerful tool as a programming language. It is easily available and is able to provide solutions in various disciplines in the field of technology like web development, Data Science or even machine learning.

A programming language is a set of instructions that enable humans to interact with computers effectively. Examples of known programming languages include Java, C#, C++ among others.

What is Python?

From the definition of what a programming language is, we can out rightly deduce that python is a programming language. But what makes it so special in the technology industry?

Python is a multipurpose or general-purpose, high level programming language. It can be used in multiple fields like web development and data science. It can also be used for processing images, texts and numbers.

History Of Python

Python was initially conceived by Guido Van Rossum in the late 1980s as a research project. In 1991 he released his first coded version of python. This code (labeled version 0.9.0) had classes with inheritance, functions, exception handling, data types and inheritance. Over the years, various developments have taken place to improved its functionalities through the Python software foundation.

What are the Main Characteristics of Python

Python, like other traditional languages, uses many of the same commands, control structures and concepts. It promotes clear coding designs, flexibility and code adheres to its principles. Some characteristics include;

It is a general-purpose language – language that is specific to a domain is intended for a specified purpose.
A high-level language – it is more readable as in other high-level languages.
Supports Object oriented programming – Python supports inheritance, classes, encapsulation, and methods.
Python programs are independent of other platforms
It is an interpreted language
Python is typed dynamically

Python Programming Guidelines

As a beginner, one is essentially required to follow the following guidelines to ensure that their code is readable and is simple;

Write one statement per line
Formulate code in an implicit way
Use consistent indentation
Label everything concisely for future reference.
Add comments to your code
Learn in-built python functions.

Applications Of python Programming

Since python is a high-level language, various applications can be derived from it. These are;

Data science,
Data analysis,
Machine learning,
Web development,
Scripting and tooling and
Artificial intelligence

These applications can be applied in various industries in creating formidable solutions around them like in medicine, finance and business intelligence, Manufacturing and even in the technology industry. Some industry applied solutions include YouTube, Pinterest, Instagram among other use cases.

What are the Pros and Cons of Learning Python as a Beginner?

While learning Python may seem like an interesting thing, it may come with limitations. These advantages and disadvantages are;

Pros of Python

It is easy to learn and use
It is universally accepted and used in various industries
Requires less coding by using simple syntax
It is free as an open-source language
It is very efficient in development of projects
Has extensive built in objects and libraries

Cons of Python

It is memory intensive
Has no multithreading support
It is prone to over use and misuse
It is slow as a language

Conclusion
Python as a high-level programming language is not only powerful to drive immense solutions to industry but is also very easy to understand. It is also a highly valuable skillset in the market today competing among other languages like Java, R, PhP, NodeJS and so on.

Technical Writing 101: Technical Ultimate Guide

Dennis Njenga — Fri, 16 Jun 2023 22:09:29 +0000

Complex technical information can sometimes be too complicated for a non-technical user. As a result of this, a user may miss important information or in the case of a manual, the user may end up doing the wrong thing. This can be frustrating and one can give up on accomplishing a task if there is no technical expert around to translate for them. This information is therefore required to be put in a simple language that users understand and can undertake a task with a lot of ease without the need for a technical expert around them.

Consider, if you went to a stall to purchase an electronic gadget that needs a connection of a few components like a desktop computer and you have never operated one let alone assemble it, you will need a technical expert to help assemble it.

What is technical writing?

The computer is packed together with a document known as the manual to help the user assemble it with ease. This manual has a step-by-step guide on how and where to connect each component for you to successfully use the computer. This guide also has images of the various points of connection which helps increase your chances of success to have it running.

Technical writing is therefore a form of writing that communicates information about specialized topics, particularly related to applied sciences and industrial products, in a simple, precise, and understandable way. It employs other aspects of technical communication such as images, videos, and graphs to improve the clarity of the message.

Writers provide guides, instructions, or explanations on technical concepts such as medical procedures, and computer applications. This form of writing is different from other writing techniques like content writing and academic writing.

What is the Difference Between Technical Writing and Other Forms of Writing

While all forms of writing are meant to send a particular kind of message, they differ in very many ways. These are the intended purpose of the content, audience, content, and the language used in the article.

Content writing is the form of writing that is meant to inform, educate or entertain a reader. This content is written for the general audience where the topics of discussion are not researched in-depth to appeal to readers.

Academic writing on the other hand is the form of writing whose goal is to show a point of view or research on a specific subject.

Technical writing is a form of writing that the content contains clear, concise, and understandable information, instructions, or guidance intended for a particular audience. The content is researched in depth and simplified. It is meant to teach and not entertain the audience.

What are the Guidelines for a Good Technical Writing

Technical writing is very important in many fields today. The content, therefore, needs to be written in a very clear, precise, and understandable way. For effective communication, a writer is required to follow a few basic guidelines;

1. Understand your Purpose
This is a very important stage and can be referred to as the pre-writing stage. It informs the direction the writing will take. At this stage, the writer is prepared on what to write through various activities;

a. Identify your audience
Knowing your audience enables the writer to determine how they will communicate the message. The audience can be grouped into;

i. Non-specialists' audience - These are users or an audience without technical know-how.

ii. Managerial or Executive audiences - They mostly use technical documents in decision-making. Writers use simplified language, graphs, images, and videos to effectively communicate concepts.

iii. Expert audience - These are audiences in the same profession such as engineers, and IT professionals. The language here may be different as opposed to the other audiences as they are at the same level hence a better understanding of concepts.

iv. Technicians - these are those that build, operate, maintain and repair what experts design.

b. Establish the scope, process, and time duration.
The writer is tasked to determine the scope, processes required, and duration for documentation of the task at hand.

c. Plan out your information or message
Before embarking on actual writing, a writer is able to draw a layout of what to communicate in the technical document. This can be achieved through looping, storyboarding, bubble-mapping, outlining, and list-making.

2. Establish the solution to a problem

Technical content is driven by problem-solving. This requires writers to understand their audience well and their various problems. Identifying a particular topic of study helps the writer understand what to research extensively in order to give clear information to the audience

3. Provide the right information
Content needs to be extensively researched for the writer to give clear and concise information derived from the topic of study.

4. Style formatting and Readability

Technical content is used for teaching and not to entertain, inspire or convince. Crafting effective and readable sentences becomes important in enabling your audience quickly understand information and act in that respect effectively. Complicated words, inappropriate tone, and poor sentence structures frustrate your audience.
To ensure effectiveness, a writer can;

i. User proper grammar
ii. Write concisely and clearly
iii. Use an active voice
iv. Avoid multiple jargon and
v. Avoid long sentences

5. Revise and re-revise
The writer needs to proofread the technical document multiple times to make sure that sentences are well crafted, and images are well placed and clear before they can publish.

Conclusion*
Technical writing is becoming an increasingly important role in business functions today. This requires writers to understand and adhere to the guiding principles of writing. The message, instructions, or guidelines should be clear, coherent, concise, concrete, correct, complete, and courteous to effectively communicate and make a good writer.

SQL101: Introduction to SQL for Data Analysis

Dennis Njenga — Sat, 18 Feb 2023 06:33:24 +0000

Introduction
SQL (Structured Query language) is an essentially powerful tool for Data Analysts. It is very helpful in instances where one is working with data held in relational databases.
SQL is useful in accessing and manipulating data allowing Data Analysts to extract valuable insights and make informed resolutions. Techniques in SQL for data analysis include; retrieving data, filtering data, joining tables, aggregating data, and creating tables.

Data Analysis

Data analysis is an undertaking that involves inspection, cleaning, transforming, and modeling data to obtain useful insights for effective decision-making. Various techniques and methods are employed to identify patterns, trends, and relationships in the data.

Relational databases

This is a type of database that stores and sorts data in a collection of related tables. It consists of one or more tables, each with a unique name, and each table consists of columns and rows.

*What is SQL? *

Pronounced as "Sequel", is a special-purpose programming language that is used to manage and manipulate data in relational databases.

History of SQL

SQL was first developed in the early 1970s by Raymond F. Boyce and Donald D. Chamberlin, who were IBM researchers. They had been working on a project referred to as System R, which was an illustration of a relational database management system (RDBMS). the goal was to build a new type of database that would be more user-friendly and flexible than existing databases.
SQL was created with the user in mind, allowing both technical and non-technical users to interact with relational databases. It was based on the principles of relational algebra and set theory, and was created to be a declarative language, which means users were required to specify what they wanted the database to do rather than how to do it.
In the 1980s, SQL was adopted as the language for accessing and manipulating data in relational databases. In 1986, the first official SQL standard was published by ANSI (American National Standards Institute) and later adopted as an international standard by ISO (International Organization for Standardization).
Why SQL for data analysis and no other technologies
SQL has grown in its popularity as an effective data analysis tool for various reasons. These are;

Flexibility – SQL offers a high rate of flexibility in data analysis. It is used to extract data, filter data aggregate data, and join data from multiple tables.
Efficient querying – SQL allows for efficient querying of large datasets enabling analysts to extract relevant data needed for analysis quickly.
Reproducibility – SQL scripts can be saved and reused making it easier to reproduce analyses and ensure that the results are consistent over time.
Standardization – SQL is a standardized language used by many different database management systems
Scalability – SQL is suitable to work with large datasets which are increasingly vital as the data volume being generated keeps growing.
Data manipulation – SQL allows for data manipulation such as adding, updating, or deleting data in a database. This offers an efficient way to clean and prepare data for analysis.
Accessibility – SQL tools are easily available for free to any user.

Components of SQL

SQL (Structured Query Language) is a domain-specific programming language used for managing and manipulating relational databases. It is comprised of several components, including:

Data Definition Language (DDL): DDL is used to define and manage the structure of the database, including tables, views, indexes, and other database objects. Common DDL statements include CREATE, ALTER, and DROP.
Data Manipulation Language (DML): DML is used to manipulate data within the database, including inserting, updating, and deleting records. Common DML statements include INSERT, UPDATE, and DELETE.
Data Query Language (DQL): DQL is used to query the database and retrieve data from one or more tables. The most common DQL statement is SELECT.
Transaction Control Language (TCL): TCL is used to manage transactions within the database. Common TCL statements include COMMIT, ROLLBACK, and SAVEPOINT.
Data Control Language (DCL): DCL is used to control access to the database and its objects. Common DCL statements include GRANT, REVOKE, and DENY.
Data Administration Language (DAL): DAL is used to manage the security, backup, and recovery of the database. Common DAL statements include BACKUP, RESTORE, and CREATE USER.

Each of these components plays a critical role in managing and manipulating data within a relational database. By leveraging these components, developers, data scientists, and analysts can work with data in a structured, efficient, and secure manner.

SQL Techniques used in Data Analysis

Understanding how SQL components work is an essential skill for any Data Analyst as well as Data scientist. To effectively analyze data in SQL, a variety of techniques are used. These are;

SELECT STATEMENTS This is the most basic SQL command that is used to retrieve data from a database. Users can specify which columns of data they want to receive and the table to retrieve from. Below is a code snippet of how the SELECT statement is used;

-- Select all columns from a table
SELECT * FROM customers;

-- Select specific columns from a table
SELECT customer_id, customer_name, email FROM customers;

-- Select a calculated column
SELECT order_id, order_date, total_amount, total_amount * 0.2 AS tax_amount FROM orders;

Aggregation SQL gives several functions that are used to summarize data such as COUNT, SUM, AVG, MAX, and MIN. These functions are used to group and analyze data based on the different conditions as per the below;

-- Count the number of rows in a table
SELECT COUNT(*) FROM orders;

-- Calculate the average order amount
SELECT AVG(total_amount) FROM orders;

-- Group orders by customer and calculate the total order amount for each customer
SELECT customer_id, SUM(total_amount) FROM orders GROUP BY customer_id;

Joins These are used to combine data from two or more tables based on identical columns or keys. There are several types of joins, these are INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN. An example of how to use joins during analysis is;

-- Inner join two tables
SELECT orders.order_id, customers.customer_name
FROM orders
INNER JOIN customers ON orders.customer_id = customers.customer_id;

-- Left join two tables
SELECT customers.customer_name, orders.total_amount
FROM customers
LEFT JOIN orders ON customers.customer_id = orders.customer_id;

-- Full outer join two tables
SELECT *
FROM customers
FULL OUTER JOIN orders ON customers.customer_id = orders.customer_id;

Subqueries This is a query within a query in a database. It allows a user to extract data from a table based on conditions from another table. For example;

-- Select all customers with orders in the past month
SELECT customer_name
FROM customers
WHERE customer_id IN (
SELECT customer_id
FROM orders
WHERE order_date > DATEADD(month, -1, GETDATE())
);

-- Select all orders with a total amount greater than the average order amount
SELECT order_id, total_amount
FROM orders
WHERE total_amount > (
SELECT AVG(total_amount)
FROM orders
);

Conditional Statements SQL offers many conditional statements that perform conditional calculations or manipulation of data. These conditions are IF, CASE, and COALESCE. For example;

-- Create a new column that indicates whether an order is a large order or a small order
SELECT order_id, total_amount,
CASE
WHEN total_amount > 1000 THEN 'Large Order'
ELSE 'Small Order'
END AS order_size
FROM orders;

-- Replace null values in a column with a default value
SELECT order_id, COALESCE(order_notes, 'No notes') AS order_notes
FROM orders;

Data cleansing SQL is used to clean and manipulate data using techniques such as trimming, filtering, and replacing data. For example;

-- Remove leading and trailing spaces from a column
SELECT TRIM(customer_name) FROM customers;

-- Filter out orders with a total amount of less than 10
SELECT * FROM orders WHERE total_amount >= 10;

-- Replace null values in a column with a specific value
SELECT order_id, REPLACE(ISNULL(order_notes, ''), 'N/A', 'No notes available') AS order_notes
FROM orders;

These are just a few techniques that an analyst can use to effectively analyze data to derive. useful insights from it.

Models Used in SQL for Data Analysis

To effectively derive insights from data, SQL models are used to structure data for efficient querying and perform calculations and aggregations. These models are;

• Relational Model: The relational model is the foundation of SQL, and it represents data as a set of tables with rows and columns. The tables are related to each other by key fields, and SQL can be used to join tables, filter data, and perform aggregations.
• Dimensional Model: The dimensional model is a specialized data model used in data warehousing. It represents data as facts and dimensions, with facts representing the measurable data (such as sales or revenue) and dimensions representing the categories or attributes that define the facts (such as time or product). SQL can be used to build and query dimensional models.
• OLAP (Online Analytical Processing) Model: The OLAP model is a data model used for multidimensional analysis, such as pivot tables or data cubes. It represents data as dimensions and measures, and SQL can be used to build and query OLAP models.
• Regression Model: Regression is a statistical model used to identify the relationship between one or more independent variables and a dependent variable. SQL can be used to build regression models, which can help to predict future outcomes based on historical data.
• Time Series Model: Time series analysis is a statistical technique used to analyze data that varies over time. SQL can be used to build time series models, which can help to identify patterns and trends in time-based data.
• Text Mining Model: Text mining is a process of extracting useful information from unstructured text data. SQL can be used to build text mining models, which can help to analyze text data and extract insights, such as sentiment analysis or topic modeling.

Importance of SQL in Data Analysis

SQL plays a very vital role in data analysis by providing a robust and standardized set of tools for retrieving, transforming, and summarizing data in relational databases. These roles are;

Data retrieval: SQL is used to retrieve data from databases. Analysts can use SQL to write queries that extract specific data from a database, which can then be analyzed and visualized using other tools.
Data transformation: SQL can be used to transform data, such as filtering, grouping, and aggregating data, to prepare it for analysis. SQL's capabilities for data transformation are essential for data cleaning and preparation, which is a crucial step in the data analysis process.
Data aggregation and summarization: SQL provides several functions for aggregating and summarizing data, such as COUNT, SUM, AVG, MAX, and MIN. These functions are essential for summarizing and understanding the characteristics of large datasets.
Joining multiple tables: SQL provides powerful join capabilities that enable analysts to combine data from multiple tables. Joining tables is a critical step in data analysis, especially for large datasets.
Data visualization: SQL can be used to retrieve and summarize data, which can then be visualized using other tools. Data analysts can use SQL to create the underlying data for charts, graphs, and other visualizations.
Data modeling: SQL can be used to create and manage data models, which define the structure and relationships of data in a database. Data modeling is an essential step in designing databases that are optimized for data analysis.
From these roles, a list of use cases can be derived. Some real-world examples of how SQL in data analysis can be used are;
E-commerce analysis: E-commerce businesses use SQL to analyze customer behavior, such as purchase history, shopping cart behavior, and website navigation. This information is used to optimize the user experience, recommend products, and personalize marketing messages. For example, an e-commerce company may use SQL to analyze shopping cart data and identify the most commonly abandoned items, allowing them to adjust pricing or shipping costs to reduce cart abandonment rates.
Financial analysis: Financial institutions use SQL to analyze customer transactions, such as deposit and withdrawal history, credit card usage, and loan payment behavior. This information is used to identify potential fraud, assess credit risk, and optimize lending decisions. For example, a bank may use SQL to analyze customer transaction history and identify patterns of suspicious behavior, such as unusual transactions or high-risk purchases.
Healthcare analysis: Healthcare organizations use SQL to analyze patient data, such as medical history, treatment outcomes, and healthcare utilization. This information is used to improve patient care, optimize healthcare delivery, and manage costs. For example, a hospital may use SQL to analyze patient outcomes for a particular treatment, allowing them to adjust treatment protocols to improve patient outcomes and reduce costs.
Marketing analysis: Marketing teams use SQL to analyze customer demographics, behavior, and preferences. This information is used to optimize marketing campaigns, personalize messaging, and improve customer retention. For example, a marketing team may use SQL to analyze customer purchase history and identify patterns in product preferences, allowing them to create targeted campaigns and promotions for specific customer segments.
Supply chain analysis: Supply chain companies use SQL to analyze inventory levels, logistics data, and shipping history. This information is used to optimize operations, reduce costs, and improve delivery times. For example, a logistics company may use SQL to analyze shipping data and identify patterns in delivery times, allowing them to adjust routes and schedules to improve efficiency and reduce costs.
Merits and Demerits of using SQL for data analysis
While SQL is a very powerful tool for data analysis, it has its strengths and weaknesses. How effective SQL depends on how well it aligns with the needs of a company.

Merits:

Speed: SQL is a fast and efficient language for retrieving, transforming, and summarizing data, and it can quickly process large datasets.
Standardization: SQL is a standard language used across many different relational database management systems, which makes it easy to learn and widely applicable.
Data Integration: SQL provides powerful join capabilities that allow analysts to combine data from multiple tables or even multiple databases. This makes it easier to integrate and analyze data from multiple sources.
Data Transformation: SQL provides a range of functions for data transformation, which can be used to clean and prepare data for analysis. These functions make it easier to standardize data and prepare it for analysis.
Security: SQL provides robust security features, including user authentication and access control, which help to protect sensitive data from unauthorized access.

Demerits:

Limited to Relational Databases: SQL is designed to work with relational databases and cannot be used to analyze data stored in other types of databases or data sources.
Limited Functionality: Although SQL provides a wide range of functions for data manipulation, it may not have all the functionality required for complex data analysis.
Complexity: SQL can be a complex language to learn, especially for those who are new to programming or have limited experience working with databases.
Maintenance: Maintaining a database and ensuring that it is up to date can be time-consuming and resource-intensive.
Requires Technical Expertise: To use SQL effectively, analysts require technical expertise in database design, data modeling, and SQL query writing.

Conclusion
SQL is a powerful tool for a data analyst, its merits outweigh its demerits.

References

SQL for Data Analysis by Cathy Tanimura
Introducing SQL: A Foundation of Data Analytics by Robb Sombach, University of Alberta, Alberta School of Business