Introduction

Data science is the study domain which deals with extracting useful information in the data. The extracted data are stored in different storage platforms which can varies from normal database, datalakes, data warehouse and data mart. The database stores the little information with small information which can be combined to store much information and upgraded to the data lake and data warehouse for big institutions and businesses. The data can be extracted by the data engineers and stored in the data warehouses for the data scientist to extract information for both analytics and building machine models.

Business domain knowledge

For a beginner to learn data science, the starting point is the knowledge in the given domain in which the individual wants to build a career in data science. The domain knowledge may include but not limited to the business, the structure of the stored data, the information on the storage behaviors and the level of accessibility for the users and the extent to which the data can be accessed through the third part application and their integration.

Learn a programming language

There are the specific programming languages used in data domain especially the data science domain such as python, Scala and R programming. The most used is the is python programming which also has different packages for extracting, cleaning and analyzing data to extract insights for the given data. The mostly used packages are NumPy, pandas, scikit-learn, matplotlib and seaborn. The NumPy is used for data manipulation especially matrix and vectors. The pandas is builds on top of NumPy and is used to manipulate the specific data from NumPy known as the DataFrame.

The DataFrame is consists of the data with columns also known as features and the row also known as observation in data science. The scikit-learn is used for machine learning and in python by predicting the different machine learning outcomes and generate machine learning insights by predicting the future outcomes from the given data. The matplotlib and the seaborn are both used for data visualization which can be used to view the behaviors of the data before sharing the stakeholders and the users in elaborating its fitness for the purpose in the organization. Apart from python, structure query language (SQL) is also important as it is used to query data from database. The SQL language is use to manipulate data from database starting by creating database, creating tables and import or exporting data from database tables.

Learning Statistics and Mathematics

Mathematics is also important in data science as data science includes different mathematical operations to manipulate data for efficient utilization. The most used mathematical operation in data science are calculus, linear algebra, probability and optimization. The calculus and linear algebra are used to manipulate the data for both row and column based which need to be turned for better data cleaning. Machine learning is also based on different mathematical formulas which are used to build machine learning models for future prediction and regression based on the obtained data through the data collection and generate insights for the present and predict the future through those data. Knowledge of machine learning for a data scientist is also important as it helps in building machine learning models and predict the future by using the past and the present data.

Learning Big Data Technologies

The knowledge of Big Data technologies is also important in the data science space as in today’s world, the data are collected with different tools that can generate data within seconds, minutes and hours like edge technologies and internet of things. As the data scientist the knowledge of the Big Data technologies such as Apache Spark, Apache Hadoop and Apache Atlas for data governance.

Learning Cloud Computing
There are different cloud technologies for cloud computing which can be used by the data scientist such as the snowflake for building datalakes, Databricks for SQL data manipulation and mostly the Big Data technologies platforms in cloud computing such as data storage, data manipulation and data querying from Amazon, Google and Microsoft through Amazon WebServices (AWS), Google Cloud (GCP) and Microsoft Azure known as Azure.

Conclusion

As a beginner in data science space, it is important to select the specific technologies to increase the consistence and knowledge specific on a given technologies starting from the prerequisite to the advanced technologies in data science.