The proper set of skills and tools is the key to success in the dynamically developing sphere of data science. The continuous innovation in fields of significance in this profession implies that one has to be not only a good technician but also a good communicator of the insights gained, a quick grasping of new technologies, and last but not least; be an out-of-box thinker equipped with problem-solving skills.

Proficiency in analytical reasoning, programming, statistical methods, and databases are among the essential skills of data scientists, according to Cindy Hubert of International Data Group.

1.Statistical Analysis & Mathematics: A strong background in statistics and mathematics is required for data scientists as per the majority of the employers. This includes probability, statistical inference, and linear regression which are of paramount importance in data analysis to be able to foresee certain patterns.

2.Programming Skills: Python and R programming languages are a MUST to have while data analysis tools such as Tableau and SAS are advantageous to have. They are commonly used for manipulating data, developing and training machine learning models, and automation of routine processes.

3. Data Wrangling: In many cases, the basic input or source data that is collected is unstructured, and thus, needs data cleaning. This is because data wrangling refers to skills that are used in mapping raw data into a useful format for analysis.

4. Machine Learning: The proper job of a data scientist also involves comprehending and applying machine learning algorithms in creating predictive models. This entails the knowledge of libraries such as TensorFlow, PyTorch, and Scikit-learn among others.

5.Data Visualization: One must be able to translate the findings into a form of presentation. Tools such as Table and Tableau, Power BI, and D3. js assist in developing outstanding graphics that convey the message so that individuals who do not understand programming language can grasp it. This is a very important part of data science as it is sometimes even of equal value to the analysis.

6.Communication and Storytelling: Unlike other profiled positions, a data scientist must also be able to present the details behind the numbers. Regardless of whether the quantity analysis relies upon appearance in front of an audience or by preparing a complete report, it is highly important to clearly and convincingly share the findings.

The top tools for data scientists are presented in the following shading: 

1. Jupyter Notebooks: It is a web-based application which is based on open source, Jupyter Notebook, to create shareable documents that combine live computer code, narratives, graphics, mathematical equations, and executable code outputs. EDX is a perfect tool for performing exploratory data analysis. 

2. GitHub: For source control and collaboration Git is just unbeatable and GitHub is the best platform to use Git. It provides the ability for Data Scientists to maintain their code as well as versioning and contribution mechanisms. 

3. Apache Hadoop and Spark: When one is faced with big data, then he or she has to work with tools such as Hadoop and Spark. They facilitate fast computation of large amounts of data over the distributed system of computers. 

4. SQL:SQL is the most important element when it comes to managing and querying data stored in relational database systems. It is one of the important skills that one has to learn for effective extraction and use of data. 

5. Docker: To deploy applications, as well as to manage environments Docker is very useful to create light, replicable, and portable environments in development and production. 

Hence, data science is not a purely technical field which means having mastery over a certain number of tools and languages or packages. The use of these skills tools and techniques not only helps the data scientist to deal with data issues but also with creating value in organizations.