Many advanced technologies, like big data, data science, artificial intelligence, and Machine learning, will dominate 2024. Read the complete article for a complete career guide for these two domains.
Big Data vs Data Science: Big Data engineers primarily focus on handling large and extensive datasets to ensure scalability and effective storage solutions by processing real-time streaming data over the internet. A Data scientist, however, extracts meaningful insights from a large pool of datasets using advanced techniques, algorithms, and tools.Â
This post will provide readers with the path and approach for a growing career in data science or big data. Read the complete post to gain insightful knowledge.
Big Data Vs Data Science: An Overview
Big data and data science are closely related fields that will show rapid expansion in 2024 and the coming years. Everything nowadays is based on data. Deriving meaningful insights and collecting data is important for an organisation to grow its business and its customers. Many of the largest companies, like Amazon, Google, Netflix, etc., are using large amounts of data to increase their sales and productivity.Â
What is Big Data?
Big data is a complex set of raw data from various sources like social sites, transactions, sensors, and more, which is handled by multiple organizations to ensure its scalability and storage solutions for real time streaming data with high speed.
Big data can be present in multiple forms, such as image, text, videos, spreadsheets, etc. However, extracting such a long source of information is a challenging task. Data analytics uses the collected data to derive useful information that helps improve company performance and efficiency.
What is Data Science?
Data Science is one of the rapid fields in recent years. Most of us have been talking about it lately, as it provides many new opportunities to seize. Data scientists are responsible for extracting meaningful insights and useful information by applying various algorithms and advanced tools. A data scientist must have some major skills and knowledge in machine learning, programming, mathematics, statistics, SQL, databases, problem-solving, etc.Â
Many big companies are always on the lookout for a skilled data scientist. Candidates who want to have a growing career as a data scientist or big data engineer need to go through the insightful information covered in this article.Â
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Big Data vs Data Science: Demand and Career OpportunitiesÂ
A big data engineer is always in high demand for many roles, such as data engineer, architect, analyst, and others. Data Science has shown exponential growth in many roles, such as Data Scientist, Business data analyst, Machine Learning engineer, and many more.Â
Also Read: 8 Computer Science and AI Skills For Future
Big Data Vs Data Science: Skills Required
The skills required to handle large amounts of data and extract information are vast, and candidates need to keep themselves updated with the latest technological advancements to make their work more effective. However, the skills required to be successful in big data and data science share a greater resemblance.
Big Data Vs Data Science: Skills Required For Big DataÂ
Some major skills required by big data engineers are mentioned below.
1. Programming LanguageÂ
Knowledge of effective programming languages is a necessary and basic requirement for any tech field nowadays. It is a fundamental skill to possess for major job roles in fields such as big data and data science.
However, Python, Java, Scala, and JavaScript are some of the popular programming languages used by millions of developers around the world. However, Python is considered one of the most efficient programming languages when it comes to handling large amounts of data and extracting information.Â
2. Big data TechnologiesÂ
You must be able to use and handle some of the important big data technologies, such as Hadoop, Apache spark, Apache Flink, etc.Â
3. Knowledge of database
Knowledge of big data requires knowledge of databases and how to handle them using NOSQL and SQL-based databases.Â
4. Data Processing and Analysis
Cleaning and processing the raw data using some analysis tools like Apache Pig and Hive is an important process in handling big data efficiently. Also, you must be able to apply machine learning algorithms and statistical methods to extract meaningful information from the data.Â
5. Data modeling and Architecture
Data models must be designed and implemented in a way to is fit for large-scale data storage. Data engineers ensure effective storage, scaling, retrieval, management, and optimization. They need to optimize the data architecture well enough to ensure optimized performance and scalability.
6. Problem solving skills
Handling such an enormous amount of data requires the personnel to have good skills and problem solving skills, using which they can create quick solutions for complex problems related to data processing, management, and storage.Â
7. Communication SkillsÂ
It is an important skill to deliver technical concepts to clients and stakeholders. A big data engineer can use tools like Tableau, Power BI, Matplotlib, and more to handle effective data visualization and help them convey important concepts easily.Â
Also Read: A/B Testing in Data Science [using Python]
Big Data Vs Data Science: Skills Required For Data Science
Data science is a field of constant learning and keeping oneself updated with the latest technologies and trends in the market. Learning data science requires strong skills in programming, databases, mathematics, statistics, etc. Let us know some of the major skill sets required by a data scientist in 2024.
1. Programming SkillsÂ
It is one of the most important skills of a data scientist. Knowledge of coding is a basic requirement to start a career as a data scientist. Many data scientists use Python as their language for handling different algorithms and tools.Â
2. Mathematics and StatisticsÂ
Knowledge of important concepts in math such as calculus, algebra, statistics, probability, and many more are required while extracting important information from the data.Â
3. Machine Learning and Artificial Intelligence
With the help of machine learning concepts, various futuristic models can be trained based on the data collected. Feature engineering, supervised learning, and training models help to make them handle human tasks easily. Various concepts, such as supervised and unsupervised learning, feature engineering, classification, regressing, and clustering, are used while performing machine learning tasks.Â
4. Data Cleaning and ProcessingÂ
It is important to clean and prepare raw and unprocessed data suitable for analysis and processing. Missing data, repetitive data, and various inconsistencies are removed in data cleaning and processing.
5. Data Visualization and Exploration
Tools like Tableau, PowerBI, and Plotly. Matplotlib, etc., helps to ensure data visualisation. A data scientist uses these tools to understand the important patterns and visualise them.
6. Problem solving skills
Handling such an enormous amount of data requires the personnel to have good problem-solving skills, using which they can create quick solutions for complex problems related to data processing, management, and storage.Â
7. Communication SkillsÂ
It is an important skill to deliver technical concepts to clients and stakeholders. Data scientists must be able to use tools like Tableau, Power BI, Matplotlib, and more to handle effective data visualisation and help them convey important concepts easily.Â
Also Read: 5 Books Every GenAI Enthusiast Should Read
Big Data Vs Data Science: Applications Areas
Big Data focuses on handling immense data, making effective storage solutions, and ensuring the stability and scalability of real-time data. While a data scientist is responsible for extracting useful insights from the dataset using machine learning algorithms and advanced tools.
Big Data Vs Data Science: Educational Qualifications and Certification Courses
A bachelor’s degree in Computer science or related fields is required for getting opportunities in Big Data. Candidates must have certification in major tools such as Hadoop Spark, Apache Pig, Apache Flink, etc.
For data science, candidates are required to keep themselves updated with the latest trends in technologies and keep updating them as needed. Some of the major qualifications are degrees in computer science, statistics, mathematics, machine learning and artificial engineering.
Hurry! Join our course at PW Skills to make candidates interview ready with industry level learning and projects. Explore our data science and big data science courses here.Â
Big Data Vs Data Science: Future Trends
Big data can see important upgrades in storage systems, processing techniques, visualization, architecture, analysis techniques, etc. However, future trends include real-time analysis, improved data handling and security, efficient management techniques, edge computing, etc.
Data Science is here to stay, it has shown a rapid expansion in recent years. Some of the future trends in data science are machine learning, artificial intelligence, advanced tools, etc.Â
Big Data Vs Data Science: Pay Scale
Many big companies require data science and big data experts who can efficiently manage big data and process it to extract useful information. The average salary in India may vary based on the company and their pay scale.
However, a skilled data engineer’s average salary can vary between 4-8 Lakh per year. In contrast, a data scientist’s average salary varies between 6-10 Lakh per year.
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Big Data Vs Data Science FAQs
What is the difference between Big data and Data science?
Big Data engineers primarily focus on handling large and extensive datasets to ensure scalability and effective storage solutions by processing real-time streaming data over the internet. A Data scientist, however, extracts meaningful insights from a large pool of datasets using advanced techniques, algorithms, and tools.
What are the major skills required to be a data scientist in 2024?
Data scientists are required to have some major skills like programming, mathematics and statistics, database management, data processing, visualizations, etc. However, a data scientist job requires constant learning and updating with the latest trends.
Is learning data science worth it in 2024?
Yes, data science is a field showing exponential growth in recent years, with many major organizations demanding skilled data science experts. Data science is showing rapid growth in the coming few years.
What are the future trends in Big data?
Big data can see important upgrades in storage systems, processing techniques, visualization, architecture, analysis techniques, etc. However, future trends include real-time analysis, improved data handling and security, efficient management techniques, edge computing, etc.
