DEV Community: Brigit Melbride

SQL 101: Introduction to Structured Query Language

Brigit Melbride — Mon, 07 Oct 2024 08:21:10 +0000

Introduction
SQL is a standard language for database creation and manipulation. It is the standard language for relational database management systems.

SQL is used to create a database, define its structure, implement it, and perform various functions on the database.

It's also used for accessing, maintaining, and manipulating already created databases. It enables you to enter, modify and extract data in a database.

WHY YOU SHOULD LEARN SQL
SQL is a crucial skill across a wide range of industries hence one of the most important tools used in data management.

SQL is a critical skill across a wide range of industries, making it one of the most important tools for data management. Whether it's for managing financial transactions, healthcare records, or tech backend systems, SQL allows organizations to access, manipulate, and analyze data effectively.
Some fields where SQL is commonly used include:

Finance: Manage transactions, customer accounts, and compliance records.
Healthcare: Maintain patient records, manage appointments, and track billing information.
Tech Industry: Store user information, manage data in applications, and power backend systems

Regardless of the industry, SQL is indispensable for data-driven decision-making.

CAREER OPPORTUNITIES IN SQL
Mastering SQL can open up several career paths, including:

Data Scientist: Examines and interprets large amounts of data to provide valuable statistical insights using complex data modeling techniques.
Data Analyst: Collects, evaluates, and interprets data to find trends and patterns, enabling better decision-making.
Business Analyst: Combines IT and business development skills to provide tailored solutions that meet business requirements.
Database Administrator: Manages and maintains company databases, ensuring data security and easy access.
SQL Developer: Designs, builds, and maintains databases for clients, often creating custom database applications.

These are just a few examples of the many career opportunities that SQL offers in today’s data-driven world.

By mastering SQL, individuals can work with databases to perform essential operations, from managing large datasets to conducting complex analyses, which are critical in today's data-driven world.

BASIC CONCEPTS OF SQL

Databases and Tables

A database is a structured collection of data that allows for easy access, management, and updating. Data within a database is stored in tables, which consist of columns and rows:

Columns represent attributes of the data (e.g., name, age).
Rows represent individual records or entries in the table.

CREATE DATABASE School;
USE School;

CREATE TABLE Students (
    name TEXT,
    age INT
);

INSERT INTO Students(name, age)
VALUES ('Melbride', 20);

In the above we create a database named School, then a table Students with two columns (name, age). Finally, we insert a new student record (Melbride, 20).

SQL Syntax

SQL uses commands to interact with data stored in tables. Commands like SELECT, INSERT, UPDATE, and DELETE allow you to perform actions on your data. SQL statements must end with a semicolon (;) to mark the end of the command.

SELECT Name, Email FROM Customers WHERE CustomerID = 1;

The above command retrieves the Name and Email of the customer with CustomerID equal to 1.

Common SQL Commands

Here are some commonly used SQL commands that are essential for interacting with databases:

SELECT: Retrieves data from one or more tables.
INSERT: Adds new data into a table.
CREATE: Creates a new table or database.
DROP: Deletes a table or database
UPDATE: Modifies existing data within a table
DELETE: Removes data from a table.

SELECT * FROM Students;
INSERT INTO Students(name, age) VALUES ('Brigit', 20);
UPDATE Students SET age = 21 WHERE name = 'Melbride';
DELETE FROM Students WHERE name = 'Brigit';

Introduction to SQL Functions

SQL also supports various functions that allow you to perform calculations and data analysis:

COUNT(): Counts the number of rows in a result set.
SUM(): Calculates the total sum of a column.
AVG(): Computes the average value of a column.
MAX(): Finds the lowest value in a column.
MIN(): Finds the highest value in a column.

SELECT COUNT(*) FROM Students;
SELECT AVG(age) FROM Students;
SELECT MAX(age) FROM Students;

Having a basic understanding of SQL, will help you to practice the simple queries and gradually explore more complex database concepts like indexing, normalization, and performance optimization. SQL is a powerful tool, and mastery comes through hands-on experience.

The Ultimate Guide To Data Analytics.

Brigit Melbride — Sun, 25 Aug 2024 20:08:34 +0000

INTRODUCTION

In today’s data-driven world, data is at the heart of everything we do. Whether it's business, healthcare, technology, or any other field, the ability to understand and analyze data has become increasingly essential. If you're considering a career in data analytics, it's crucial to have a deep interest in data, a willingness to learn, and a passion for uncovering insights hidden within vast datasets.

This guide will walk you through the fundamentals of data analytics, providing you with the knowledge and tools you need to succeed in this exciting field. But first, let’s start with a clear definition.

definition Data analytics involves using data, techniques and tools that identify patterns and trends to generate insights that support decision making. Data analytics extracts actionable insights from data to enable organizations, companies to make informed decisions.

Fundamentals Of Data Analytics

Data analytics relies on four fundamental processes to ensure effective analysis. These processes, outlined below, are essential for deriving meaningful insights from data.

Data Collection: Involves getting data from different places such as websites, survey or business records.

Data cleaning: This process involves cleaning up data by handling missing values, duplicated values, and putting the data in order so it's easier to look.

Analyzing the data: This involves using math and computer programs to find patterns, trends, or interesting in the data. This includes creating charts, graphs, performing statistical summaries.

Making decisions: Understanding what the data tells us and using the information to make choices or plan what to do next.

Types Of Data Analytics
There are mainly 4 types of data analytics in which each answer a key question about your data's journey;

Descriptive Analytics: Looks at past data to tell you what happened before.
Diagnostic Analytics: This analytics tries to figure out why something happened.
Prescriptive Analytics: Suggests what you should do based on the data analysis.
Predictive Analytics: makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.

Tools and Technologies Used in Data Analytics

Data analytics relies on a diverse set of tools that help professionals gather, clean, analyze, and derive insights from data. These tools are essential for efficiently handling data at various stages of the analytics process.

Databases: Databases are essential for storing, organizing, and managing large volumes of data, making it easy to retrieve and manipulate as needed such as MYSQL, Microsoft SQL Server, MongoDB.
Programming Languages: They provide the foundation for implementing data analysis, allowing for the automation of data processing and the application of statistical and machine learning techniques. R is designed for data analysis, statistical modeling, and visualization. Python contain libraries such as Pandas, NumPy, SciPy that are used for data manipulation, analysis, and visualization.
Data visualization tools: Visualization tools help in translating complex data into graphical representations, making it easier to understand trends, patterns, and insights. Tools such as Tableau which allows users to create interactive and shareable dashboards, Power BI that enables users to create reports and dashboards, Matplotlib provides the foundational plotting tools, while Seaborn builds on it to offer more attractive and informative statistical graphics.
Big Data Technologies: Technologies like Hadoop, Spark are designed to handle, process, and analyze vast amounts of data that exceed the capabilities of traditional database systems.
Advanced Analysis: Machine learning tools such as tensorFlow and scikit-learn are used to train machines to be able to learn from data to make predictions or understand patterns.
Cloud Analytics: Cloud-based analytics platforms provide the flexibility to perform data analysis on the cloud, enabling scalability and reducing the need for powerful on-premises hardware.

Big Data Analytics

This refers to the methods, tools, and applications used to collect, process, and derive insights from varied, high-volume, high-velocity data sets. The datasets come from various sources such as social media, email.
It has characteristics like Velocity, Variety, Volume and Veracity. The architecture that includes data warehouses and data lakes. When working with big data you may encounter challenges such as Scalability, data quality and real-time processing.

Applications Of Data analytics

Data analytics is applied in various fields as shown below;

Banking: Banks employ data analytics to manage risks, gather insights into customer behavior, and personalize financial services.
Healthcare: Data analytics positively changes the healthcare industry by offering better patient care, disease prevention, and resource optimization. Hospitals can analyze patient data to spot high-risk individuals and provide personalized treatment plans.
Finance: It plays a vital role in investment strategies, fraud detection, and risk assessment. Banks and other financial institutions analyze vast volumes of data to predict creditworthiness, optimize their investment portfolios.
Manufacturing: Allows manufacturers to monitor production lines in real time, leading to higher productivity and savings.
Business Intelligence: It is used for strategic decision-making, customer segmentation, market analysis, and performance optimization. Helps businesses understand customer behaviour, preferences, and trends.
Marketing: data analytics is used for targeting the right audience, measuring campaign effectiveness, and optimizing marketing strategies.

Essential Skills for Data Analysts

In order to succeed in your data analyst career you have to master these skills; technical and non-technical as listed below;

i.Programming languages: In data analytics, languages like Python, R and SQL are indispensable. They allow you to manipulate data, perform statistical analyses, and create data visualizations. SQL is for database management, it allows you to query, update, and manipulate structured data.

ii.Data Visualization Tools: Data visualization is all about creating charts, telling a story with data. Tools like Tableau and Power BI are widely used for to allow you to transform complex data into easily digestible visual formats.

iii.Statistical Analysis: This is the backbone of data analytics since it provides the methodologies for making inferences from data. Statistical methods allows you to use descriptive statistics to summarize and interpret data, Inferential statistics to make predictions and inferences about a population on a sample and Hypothesis testing to evaluate theories with a view of solving practical problems.

iv.Data Wrangling and Cleaning: Mastering this skill will help you a lot to clean and transform your data into a usable format before doing any analysis.

v.Problem-solving: When doing your analysis, you will often face ambiguous problems that will require your analytical thinking. You will need to apply critical thinking, analytical reasoning and innovation to be able to solve this problems.

vi.Communication skills: In data analytics, communication is not just about presenting your findings; it's about translating complex data into actionable insights that can be easily understood by non-technical stakeholders. For an effective communication, be able to apply data storytelling to weave data into a compelling narrative that drives business decisions, be able to present your data visually and verbally and build a good relationship with team members and stakeholders.

vii. Pay attention to every detail in your analysis steps from the start to the end.

Career Opportunities in Data Analytics

1. Data Scientist: Working to deeply understand and analyze data to provide actionable insights. Here you'll collect, analyze and interpret data to provide actionable insights, use statistical and ML techniques to identify patterns and trends in data, make predictions and inform business decisions. Be proficient in programming languages, have knowledge in statistics or mathematics.

2. Data Engineer: Data Engineer involves designing and building the infrastructure and systems that support data collection, storage, and analysis. Managing and maintaining large datasets and databases. Ensuring data is accurate, accessible, and secure. Be proficient in programming languages; Python, Java, SQL have an experience with big data technologies such Hadoop and Spark.

3. Financial Analyst: A financial analyst is responsible for collecting and analyzing financial data to inform business decisions, communicating insights, identify trends and patterns in financial data and develop financial models and forecasts.
Have a strong analytical and problem-solving skills, experience with financial modelling and analysis, strong communication and presentation skills and know SQL, Excel, R, and Python.

4. Business Intelligence Analyst: This career involves collecting and analyzing data to help organizations make better business decisions. Using data visualization software to present data in a way that is easy for non-technical stakeholders to understand. You need a background in business or a related field, experience with data warehousing and BI tools, SQL, Excel and Tableau.

5. Business Analyst: A business analyst is responsible for identifying and analyzing business problems and opportunities, develop solutions to improve business processes and performance, and communicate with stakeholders to gather requirements and recommendations. He / She requires skills such as strong analytical and problem-solving, Excellent communication and presentation skills, project management and SQL, Excel and Tableau.

There are so many other careers you can do as a data analysts; we have careers like Data Visualization Engineer, Data Governance Analyst, Risk Analyst, Quantitative Analyst, Marketing Analytics Manager. As long as you set your mind in data analyst then you won't miss a chance.

Emerging Trends In Data Analytics

Data analytics has continued to evolve rapidly in the field of technology, driving significant changes in how businesses operate and make decisions. Some of the key emerging trends in data analytics include:

AI-Powered Data Analytics- It is responsible for the change in business operations in the current world. AI has enhanced the visualization and analysis of data, along with improving human data handling abilities in data analytics.
Edge Computing: A range of devices and networks near the users. It offers a path to collect data from devices via low-latency connectivity, high-performance processing, and secure platforms. It speeds up the data travel from a device to a nearby edge.
Augmented Analytics: This has been the most trending predictive analytics in the modern word analytics. It uses NLP and ML to automate and process data.
Generative AI: Techniques like generative adversarial networks that can produce synthetic yet realistic data to augment analytics and train ML models.
Automated ML: Platforms with inbuilt ML and AI that can automatically test different models and algorithms to deliver optimized analytics solutions without intensive coding.
Apache Spark: This is an open-source framework that is built for large scale stream processing, and it integrates with popular languages such as Python, R and Scala.
Kafka: Is the leading open-source event streaming platform that allows real-time data pipelines and streaming analytics applications.

CONCLUSION

Data analytics plays a crucial role in identifying trends and metrics that might otherwise go unnoticed within vast amounts of information. By optimizing processes based on these insights, businesses can improve efficiency and make more informed decisions. However, it's essential to practice data governance, adhere to regulations, and ensure proper metadata management and data observability while working with data.

Future Engineering Ultimate Guide.

Brigit Melbride — Sun, 18 Aug 2024 19:20:25 +0000

INTRODUCTION

Engineering is the key to unlocking a sustainable and prosperous future. As we look towards the future, the role of engineers in shaping the world becomes even more critical with the increasing pace of technological advancements. Engineers are at the forefront of innovation, solving problems in order to create a better world.

Future engineering involves specific techniques and knowledge of the field in which both work together with technology. Artificial intelligence and digital tools are reshaping engineering demanding new skills and agility. Engineers are trained to develop solutions and to take responsibility for the execution of projects in various sectors.

The future of engineering is filled with exciting possibilities. With advancements in new-age technologies such as AI, virtual reality and many more. In this article we will explore this emerging technologies from AI-driven solutions to sustainable design practices and much more.

EMERGING TECHNOLOGIES IN ENGINEERING.

1. Artificial Intelligence and Machine Learning: AI is rapidly evolving and becoming deeply integrated into people's daily lives. It serves as the backbone of modern innovation, enabling digital computers or computer-controlled robots to perform tasks associated with human beings intelligence. From speech recognition to decision making.

Machine learning which is a subset of AI, uses algorithms that can learn from data and improve the performance of the learning models overtime. The models adapt and learn as they process more data. AI and ML are now employed across various industries, from entertainment (such as gaming solutions) to customer service (like chatbots and sentiment analysis), and in the development of autonomous vehicles, virtual assistants, healthcare diagnostics, and more.

_2. Internet of Things(IoT):_Refers to a network of linked gadgets with the ability to communicate and exchange data. In the future IoT will play a crucial role into enabling change in business process; using sensor devices. The government sector will be able to apply IoT in there utilities to notify their users of mass outages and small interruptions of water or power. In manufacturing, manufacturers can quickly check equipment for accuracy by use of sensor alerts.

3. Robotics: This is the interdisciplinary study and practice of the design, construction, operation, and use of robots. It includes various fields such as mechanical engineering, electrical engineering, computer science, and artificial intelligence to create machines that can perform tasks autonomously or semi-autonomously. In mechanical engineering, robotics focuses on the design and construction of the physical structures of robots, ensuring they can interact with the physical world efficiently and safely.

4. Augmented reality: This is the integration of digital information with the user's environment. It continues to be developed and becomes more pervasive among a wide range of applications. AR users experience a real-world environment with generated perceptual information overlaid on top of it. Retailers and other companies are able to use AR to promote products or services, collect user data. Google maps offers an AR navigation of the cities.

5. Cybersecurity: Computer security is the protection of computer software, systems and networks from threats that may result in unauthorized information disclosure, theft of hardware, software, or data, as well as from disruption. With the increase in interconnectedness of systems, cybersecurity will become a critical concern for engineers. Protecting users from cyber threats will be paramount.

6. Big data: Data is being generated at a higher rate. Large and complex datasets that can be analyzed to extract valuable insights. Big data analytics will optimize systems, predict failures, and improve performance.

Big data is used to improve various projects, such as urban planning where engineers analyze data to optimize traffic low and reduce congestion, also in manufacturing to predict equipment failures, improving overall productivity and reducing downtime.

ETHICAL CONSIDERATIONS IN FUTURE ENGINEERING

The role of ethics in engineering becomes increasingly important as technology advances.

Safety First: Prioritize the safety, health, and welfare of the public above all considerations.

Data Privacy and Cybersecurity: With the rise of big data and IoT, protecting user data has become a critical ethical concern. Design systems that safeguard personal information and prevent cyber threats.

Avoiding Conflicts of Interest: Ensure personal, financial, or other interests do not compromise or bias professional judgment.

EMBRACING THE FUTURE OF ENGINEERING.

Engineers are the solution seekers to the complex challenges our modern world faces. If you're interested in engineering or already in the field, it's important to stay up to date with the latest technologies and trends. Doing so will equip you with the skills needed to remain competitive in an evolving job market.

Staying informed about emerging technologies and trends in engineering will enhance both your academic and professional growth.

NOTE The future of engineering is bright, and it promises to revolutionize the way we design, build, and innovate in the years to come.

Understanding Your Data: The Essentials of Exploratory Data Analysis.

Brigit Melbride — Fri, 09 Aug 2024 21:25:31 +0000

INTRODUCTION

As data scientists and data analyst, This is a very very important and crucial initial step that must be performed. After data collection, data is in raw form and unprocessed facts a data scientist, analyst, or any other person is unable to understand the structure and contents of that data, That's where EDA comes in; analyzing and visualizing data to understand its key characteristics, uncover patterns, and identify relationships between variables.

Understanding data requires understanding the expected qualities and characteristics of data. The knowledge you have about data, the needs the data will satisfy, it's content and creation. Let's now dive deeper into EDA to understand how we should transform data to be understood.

EXPLORATORY DATA ANALYSIS

As defined above, EDA refers to analyzing and visualizing data to understand it's key characteristics, uncover patterns, and identify relationships between variables. It helps determine how best to manipulate data sources to get answers you need, making it easier for data scientists to discover patterns, spot anomalies, test hypothesis or assumptions. It is an important first step in data analysis, it is the foundation for understanding and interpreting complex datasets.

TYPES OF EDA
These are different methods and approaches used within exploratory data analysis process. Here are three main types of EDA:

Univariate Analysis: This is the simplest form you can use to analyze data, It explores each variable in a dataset. Involves looking at the range of values, as well as the central tendency of the values. It describes the pattern of response, each variable on its own For example, examining the age of employees in a company.

Bivariate Analysis: This analysis, two variables are observed. It aims to determine if there is a statistical link between the two variables and if yes how strong are they. Bivariate lets researchers look at the relationship between two variables. Before using this analysis, you have to understand why it is important;

 Bivariate analysis helps identify trends and patterns
 Helps identify cause and effect relationships.
 Helps researchers to make predictions.
 It also inform decision-making.

Techniques used in bivariate analysis include scatterplots, correlation, regression, chi-square tests, t-tests, and analysis of variance which can be used to determine how two variables are related.

Multivariate Analysis: This involves the statistical study of experiments in which multiple measurements are made on each experimental unit and for which the relationships among multivariate measurements and their structure are important to the experiment's understanding. For example, How many hours a day a person spends on Instagram.

Techniques include dependence techniques and interdependence techniques.

ESSENTIALS OF EDA

a. Data collection: The first step when dealing with data is first having the data you want. Data is gathered from various sources according to the topic you're working on, using methods like web scraping or downloading datasets from platforms such as Kaggle.

b. Understanding your data: Before proceeding to cleaning, you first have to understand the data you collected. Try to understand the number of rows and columns you'll be working with, the information for each column, the characteristics of your data, data types and much more.

c. Data cleaning: This step involves identifying and addressing errors, inconsistencies, duplicates, or incomplete entries within the data. The main objective of this step is to enhance the qualities and usefulness of data hence leading to more dependable and precise findings. Data cleaning involves several steps;
How to clean data;

      i)Handling missing values: by imputing them using mean, mode, median of the column, fill with a constant, forward-fill, backward-fill, interpolation or dropping them using the dropna() function.

      ii)Detecting outliers: you can detect outliers using the interquartile range, visualizing, using Z-Score or using One-Class SVM.

      iii)Handle duplicates: Drop duplicate records

      iv)Fix structural errors: Address issues with the layout and format of your data such as date formats or misaligned fields.

      v)Remove unnecessary values: Your dataset might contain irrelevant or redundant information that is unnecessary for your analysis. You can identify and remove any records or fields that won't contribute to the insights you are trying to derive.

d. Summary statistics. This step provides a quick overview of the dataset's central tendencies and spread, including mean, median, mode, standard deviation, minimum, maximum using the describe method in pandas or numpy for numeric features. For categorical features we can use graphs and actual summary statistics.

e. Data visualization: This is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data. Try to identify trends and patterns in the dataset, using lines, bars, scatter and box plot with tools like matplotlib, seaborn or tableau.

f. Data relationship. Identify the relationship between your data by performing correlation analysis to examine correlations between variables.

Analyze relationships between categorical variables. Use techniques like correlation matrices, heatmaps to visualize.

g. Test Hypothesis: Conduct tests like t-tests, chi-square tests, and ANOVA to determine statistical significance.

h. Communicate Your findings and Insights: This is the final step in carrying out EDA. This includes summarizing your evaluation, highlighting fundamental discoveries, and imparting your outcomes cleanly.

Clearly state the targets and scope of your analysis.
Use visualizations to display your findings.
Highlight critical insights, patterns, or anomalies you discovered in your EDA.
Discuss any barriers or caveats related to your analysis.

The next step after conducting Exploratory Data Analysis (EDA) in a data science project is feature engineering. This process involves transforming your features into a format that can be effectively understood and utilized by your model. Feature engineering builds on the insights gained from EDA to enhance the data, ensuring that it is in the best possible form for model training and performance. Let’s explore feature engineering in simple terms.

Feature Engineering.

This is the process of selecting, manipulating and transforming raw data into features that can be used in model creation. This process involves 4 main steps;

Feature Creation:- Create new features from the existing features, using your domain knowledge or observing patterns in the data. This step helps to improve the model performance.

Feature Transformation: This involves the transformation of your features into more suitable representation for your model. This is done to ensure that the model can effectively learn from the data. Transforming data involves 4 types;

 i) Normalization: Changing the shape of your distribution data. Map data to a bounded range using methods like Min-Max Normalization or Z-score Normalization.

 ii) Scaling. Rescale your features to have a similar scale  to make sure the model considers all features equally using methods like Min-Max Scaling, Standardization and  MaxAbs Scaling.

 iii) Encoding. Apply encoding to your categorical features to transform them to numerical features using methods like label encoding, One-hot encoding, Ordinal encoding or any other encoding according to the structure of your categorical columns.

 iv) Transformation. Transform the features using mathematical operations to change the distribution of features for example logarithmic, square root.

Feature Extraction: Extract new features from the existing attributes. It is concerned with reducing the number of features in the model, such as using Principal Component Analysis(PCA).
Feature Selection: Identify and select the most relevant features for further analysis. Use filter method( Evaluate features based on statistical metrics and select the most relevant ones), wrapper method(Use machine learning models to evaluate feature subsets and select the best combination based on model performance) or embedded method(Perform feature selection as part of model training e.g regularization techniques)

Tools Used for Performing EDA

-Let's look at the tools we can use to perform our analysis efficiently.

Python libraries

         i)   Pandas: Provides extensive functions for data manipulation and analysis.

         ii)  Matplotlib: Used for creating static, interactive, and animated visualizations.

         iii) Seaborn: Built on top of Matplotlib, providing a high-level interface for drawing attractive and informative capabilities.

         iv)  Plotly: Used for making interactive plots and offers more sophisticated visualization capabilities.

R Packages

     i)  ggplot2: This is used for making complex plots from data 
      in a dataframe.

    ii)  dplyr: It helps in solving the most common data manipulation challenges.

   iii)  tidyr: This tool is used to tidy your dataset; Storing it in a consistent form that matches the semantics of the dataset with the way it is stored.

Conclusion
Exploratory Data Analysis (EDA) forms the foundation of data science, offering insights and guiding informed decision-making. EDA empowers data scientists to uncover hidden truths and steer projects toward success. Always ensure to perform thorough EDA for effective model performance.

Expert Advice on how to Build a Successful Career in Data Science.

Brigit Melbride — Sat, 03 Aug 2024 23:51:26 +0000

INTRODUCTION

This is an Industry that can be immensely rewarding, providing an intellectually challenging and stimulating environment. Data scientists must keep ahead of the latest and emerging technologies and developments. Building a successful career in this field is not as hard as some people think and it's not easy, it requires focus mind, curiosity and analytical mind, time and a lot more.

Data Science Skills You Need To Know

To succeed in data science you'll need an assortment of technical and non-technical skills.

Technical skills- skills you'll need to develop to become a data scientist;

   1. Programming: Be proficient in Python and R which are mostly used in data manipulation, statistical analysis and machine learning.

   2. Mathematics: Have knowledge in linear algebra, calculus and optimization techniques which are essential in machine learning and algorithms.

   3. Data visualization: Be able to transform your data and findings into visual formats using tools like Power BI and Python libraries like seaborn and matplotlib.

   4. Machine learning: You should be able to apply and understand different machine learning algorithms like supervised and unsupervised learning to predict outcomes and show patterns.

   5. Data Transformation: Transforming your data into more useful form using feature engineering techniques and normalization.

   6. Data Wrangling: Ability to clean, structure and enrich raw data into a desired format for analysis. Handling missing values, outliers and merging datasets.

   7. Deep Learning: Understand neural networks and deep learning frameworks for tasks that require image recognition, NLP.

   8. Model Deployment: Skills on how to deploy your models into production environments.

  9.  Business Intelligence: Use BI tools and techniques to analyze data, produce reports and decision making.     

  10. Analytics and Modeling: Be able to apply various analytical and modeling techniques to understand data, make predictions, and test hypotheses.

  11. Big Data: Manage and analyze large volumes of data with big technologies, understand the complexities and challenges of big data environments.

2.Non-Technical skills- These are the human skills, they are cross-functional that are very necessary;

Communication: Being able to translate your findings into clear, concise, and actionable insights for technical and non-technical stakeholders.

Business Acumen: Understand business processes, goals and strategies to align data projects with organizational objectives.

Teamwork: Engage and work well with your team members, always participate and contribute to the work.

Critical Thinking: The ability to approach problems logically, question assumptions, and evaluate the strengths of arguments.

Curiosity: A natural curiosity to ask questions, explore data for hidden patterns, and a continuous desire to learn and discover new techniques.

Problem-solving: Defining a problem, determining the cause of the problem, prioritizing and selecting alternatives for a solution.

Time management: Manage your time well. Plan which topic or which side you'll tackle first, focus to finish the task. Use at least 4hours a day just for coding because the more you code the more it sticks in your mind.

Tips to Become A Data Scientist

To make your career plans easier to navigate, here are a few major tips to become a Data Scientist;

a. Fundamentals: Start by learning the basics, you can't start from the advanced concepts, gaining the fundamental knowledge will provide you with a strong foundation for advanced concepts.

b. Programming Skills: Familiarizing yourself with one or two languages and gaining proficiency in them will help you a lot in going forward.

c. Build a strong foundation in data manipulation: Learn the techniques of cleaning, transforming, and preparing data for analysis.

d. Focus on Machine Learning: Have a strong foundation in ML and learn about the various algorithms and their working mechanism.

e. Build Your Portfolio: Having a portfolio is always the important thing as a tech person because that is where you showcase your skills and projects you have done from the moment you began your career.

g. GitHub account: Make sure you create a GitHub account to keep all of your projects

h. Network and Learn from Others: Always be ready to network and to learn from anyone. Attend meetups, hackathons, conferences in order to be updated with the latest trends. Networking helps a lot and it has worked for so many techs I being among them. This includes connecting via social media accounts like LinkedIn.

i. Practice critical thinking: This helps in analyzing data and solving complex problems. You can develop this skill by asking questions, testing hypothesis and challenging assumptions.

j. Develop communication skills: Data scientists need to communicate complex findings to non-technical stakeholders

k. Practice ethical data science: As you access data practice ethical data science by ensuring data privacy, accuracy, and fairness.

EDUCATIONAL BACKGROUND.

Building a Data Science career is a journey from several educational backgrounds. Everyone prefers a certain platform that works best for him/her.

Earning a degree from a certain university is a good start for a data science course but it is not the best since you're not always taught everything. The university gives you 25% and 75% is yours to do.
We also have free online websites that offers free courses and certifications in data science from being a beginner to expert sites like Coursera, edX, Udemy, FreeCodeCamp and many more.
There are also Data science Bootcamps that offer intensive training to help you quickly acquire practical skills. Udemy, Coursera is among the sites that offers Data Science Bootcamps.
YouTube is one of the best platforms you can learn from, its like an online university as long as you have an internet connection. It has everything you need about data science that is tutorials coding together with the tutor helps you understand more about what you're doing.

JOB SEARCHING AS A DATA SCIENTIST.

There are many data scientist who have graduated from good universities but they keep on searching for jobs. The easiest way to find a job as a data scientist is to give a client what they need.

CONCEPTS
Have a Portfolio: If you're looking for a serious job in this field, do some projects with real data. You can post them on your GitHub account that you must Have. Apart from Kaggle competitions, Zindi competitions, find something that you love or a problem you want to solve and use your knowledge to do it.

Look for an Internship before you start applying for a job. This help you to have an experience before doing the real job. Also you will have something to place on your resume.

Be Patient: If you apply and don't get the job, be patient keep trying as long as you have the necessary skills then there is no doubt you'll get a job.

Be Prepared: In any skill be it technical or non-technical, remember that you'll be important in a certain organization, dealing with different people, be ready to answer questions about how you'll behave in different situations at work.

LinkedIn Account: Ensure to have a LinkedIn account. This will help you to connect with different data scientists around the globe as you share your knowledge a client might be interested in your work and he/she reaches out to you.

Ask and search different data scientists to know what they do at work and how they got to secure there jobs in order to have an idea of what to do.

NOTE Always keep in mind that;

Practice makes perfect
Put what you learn into practice by doing projects, participating in competitions, hackathons. Share what you've built, be ready to learn, corrected, disappointed and more. Kaggle, Zindi, Analytics Vidhya, hackerEarth, Devpost and many more hosts knowledge competitions, hackathons you can join to boost your skills, earn badges and learn more.

As long as you're in Tech field, new technologies will keep on emerging, keep learning.
Good luck! in your Data Science Career.....