Introduction
Hey there! Today I will be sharing what I believe to be the key skills an expert in Data Science should posses. This will be a follow through to the article I did for the beginner's guide so if you missed it, I will share the link below.
[(https://dev.to/michellenjeriscientist/demystifying-data-science-a-beginners-guide-pa6)]
According to Wikipedia ,Data Science is an interdisciplinary field focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains.
A Data Scientist is responsible for many roles in an organization like business analytics, building data products, developing visualizations, ML algorithms and more. I do believe the first step is to actually understand the different roles of different data specialists so that you know what peaks your interest. These data specializations are; data analyst, data scientist, data administrator, data architect, business analyst, business intelligence manager, data/analytics manager.
The following are important skills you must have as a data scientist:
1. Mastering programming languages like R or Python
Python and R are both free, open-source languages. They both, run smoothly on most common operating systems i.e. Linus, macOS and Windows. Both languages have a long list of functionalities and can easily take on any data analysis task. Beginner or expert, the languages are easy to learn and execute.
Python is a general-purpose, object-oriented programming language. Its easy syntax makes it perfect for collaborations. It ensures smooth execution of tasks with flexibility, stability and code readability.
R is a popular statistical programming language that is built to facilitate computing and data visualization. R has numerous abilities, including statistical analysis, visualization of data and manipulating data.
You can compare the two and choose what best works for you.
2. Statistics and Applied Mathematics.
Statistics and data science are fields of applied mathematics designed to interpret data in all its many forms. This is done by applying mathematical models and statistical theory that relate the data at hand to the underlying questions and often hidden features of interest. A key advantage of statistical science is the ability to quantify the uncertainty in a prediction or decision and for decision making this aspect is often as important as the estimate itself.
A simpler summary of this from Wikipedia From hypothesis testing to regression analysis, statistical methods enable professionals to validate hypotheses, quantify uncertainties, and draw conclusions with confidence.
3. Working Knowledge of Hadoop and Spark.
Apache Hadoop is an open-source software utility that allows users to manage big data sets by enabling a network of computers to solve vast and intricate data problems.
It is a highly scalable, cost-effective solution that stores and processes structured, semi-structured and unstructured data.
Benefits of the Hadoop framework include the following:
- Data protection amid a hardware failure.
- Vast scalability from a single server to thousands of machines.
- Real-time analytics for historical analyses and decision-making processes.
Apache Spark is a data processing engine for big data sets. Like Hadoop, Spark splits up large tasks across different nodes. However, it tends to perform faster than Hadoop and it uses random access memory ( to cache and process data instead of a file system. This enables Spark to handle use cases that Hadoop cannot.
Benefits of the Spark framework include the following:
- A unified engine that supports SQL queries, streaming data, machine learning (ML) and graph processing.
- Can be 100x faster than Hadoop for smaller workloads via in-memory processing, disk data storage, etc.
- APIs designed for ease of use when manipulating semi-structured data and transforming data
These systems are two of the most prominent distributed systems for processing data on the market today. Hadoop is used mainly for disk-heavy operations with the MapReduce paradigm, and Spark is a more flexible, but more costly in-memory processing architecture.
4. Databases: SQL and NoSQL.
As a data scientist, it is generally recommended to learn both SQL and NoSQL databases, as they serve different purposes and are often used in complementary ways.
SQL (Structured Query Language) databases, such as PostgreSQL, MySQL, and Oracle, are well-suited for structured, tabular data and are widely used for data storage, management, and retrieval. They excel at performing complex queries, ensuring data integrity, and supporting transactions.
NoSQL databases, such as MongoDB, Cassandra, and Elasticsearch, are designed to handle unstructured, semi-structured, or rapidly changing data that does not fit well into the rigid structure of traditional SQL databases. NoSQL databases offer features like horizontal scalability, flexible schema, and high availability.
As a data scientist, having expertise in both SQL and NoSQL databases can be advantageous, as it allows you to choose the appropriate database technology for a given problem or dataset. By learning both SQL and NoSQL, data scientists can:
- Gain a deeper understanding of data storage and management techniques.
- Become more versatile in adapting to different data requirements and use cases.
- Leverage the strengths of each database type to build robust and scalable data solutions.
- Seamlessly integrate SQL and NoSQL databases within their data architecture.
- Enhance their ability to work with diverse data sources and formats.
5. Machine Learning and Neural Networks.
Machine learning and data science are inextricably linked. Machine learning is defined as a machine's ability to extract knowledge from data. Machines can't learn much if they don't have any data. If anything, the growing use of machine learning in a variety of industries will act as a catalyst for data science to grow dramatically. Data scientists are expected to have a basic understanding of machine learning.
From Wikipedia a neural network is a method for performing machine learning tasks, training a computer with labelled training data. In other words, a computer program can learn to make decisions based on a model it builds from a training dataset._ A common goal for data scientists in artificial intelligence is to be able to classify data and associate data to different categories. Neural networks help us develop powerful algorithms that can achieve this.
6.Proficiency in Deep Learning Frameworks
Deep learning is a subset of ML that has been proven successful in helping recognize data patterns. It’s termed as a neural network-based approach that will allow computers to learn to do things independently rather than being programmed by humans.
Experts forecast deep learning to become the dominant technique for data analysis in the coming few years. Its impact on data science will be significant.
Deep Learning algorithms can learn more from data than traditional ML analytics and algorithms, because they can learn not only from data input but also from the hidden layers of data that will present higher-level concepts.
In addition, deep learning algorithms would be trained on massive datasets, which gives them an advantage over traditional ML algorithms which are struggling with big data.
Deep learning is likely to become the dominant data analysis technique across all domains in the near future.
7. Creative Thinking & Industry Knowledge.
A data scientist requires a foundation of technical skills. These include the ability to interpret, manipulate, and extract meaning from data, and then use it to build predictive models and generate business insights. Creativity in data science can be seen in anything from innovative modeling, thinking up original ways to collect data, developing new tools, and being able to visualize data process a few years down the line.
Conclusion
Learning all these skills will take time, but they are definitely essential for wholesome growth in the Data Science field. Take your time, learn your track, put in the work and watch the magic unfold.
Till next time!
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