Knowledge and Skills for Data Science Professionals

What is Data Science?

Data can be proved to be very fruitful if we know how to manipulate it to get hidden patterns from them. This logic behind the data or the process behind the manipulation is what is known as Data Science. From formulating the problem statement and collection of data to extracting the required results from them the Data Science process and the professional who ensures that the whole process is going smoothly or not is known as the Data Scientist. But there are other job roles as well in this domain as well like:...

Components of Data Science Process

Data Science is a very vast field and to get the best out of the data at hand one has to apply multiple methodologies and use different tools to make sure the integrity of the data remains intact throughout the process keeping data privacy in mind. Machine Learning and Data analysis is the part where we focus on the results which can be extracted from the data at hand. But Data engineering is the part in which the main task is to ensure that the data is managed properly and proper data pipelines are created for smooth data flow. If we try to point out the main components of Data Science then it would be:...

Knowledge and Skills for Data Science Professionals

As a Data Scientist, you’ll be responsible for jobs that span three domains of skills....

Steps for Data Science Processes:

Step 1: Defining research goals and creating a project charter...

Benefits and uses of data science and big data

Governmental organizations are also aware of data’s value. A data scientist in a governmental organization gets to work on diverse projects such as detecting fraud and other criminal activity or optimizing project funding. Nongovernmental organizations (NGOs) are also no strangers to using data. They use it to raise money and defend their causes. The World Wildlife Fund (WWF), for instance, employs data scientists to increase the effectiveness of their fundraising efforts. Universities use data science in their research but also to enhance the study experience of their students. • Ex: MOOC’s- Massive open online courses....

Tools for Data Science Process

As time has passed tools to perform different tasks in Data Science have evolved to a great extent. Different software like Matlab and Power BI, and programming Languages like Python and R Programming Language provides many utility features which help us to complete most of the most complex task within a very limited time and efficiently. Some of the tools which are very popular in this domain of Data Science are shown in the below image....

Usage of Data Science Process

The Data Science Process is a systematic approach to solving data-related problems and consists of the following steps:...

Issues of Data Science Process

Data Quality and Availability: Data quality can affect the accuracy of the models developed and therefore, it is important to ensure that the data is accurate, complete, and consistent. Data availability can also be an issue, as the data required for analysis may not be readily available or accessible. Bias in Data and Algorithms: Bias can exist in data due to sampling techniques, measurement errors, or imbalanced datasets, which can affect the accuracy of models. Algorithms can also perpetuate existing societal biases, leading to unfair or discriminatory outcomes. Model Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too well, but fails to generalize to new data. On the other hand, underfitting occurs when a model is too simple and is not able to capture the underlying relationships in the data. Model Interpretability: Complex models can be difficult to interpret and understand, making it challenging to explain the model’s decisions and decisions. This can be an issue when it comes to making business decisions or gaining stakeholder buy-in. Privacy and Ethical Considerations: Data science often involves the collection and analysis of sensitive personal information, leading to privacy and ethical concerns. It is important to consider privacy implications and ensure that data is used in a responsible and ethical manner. Technical Challenges: Technical challenges can arise during the data science process such as data storage and processing, algorithm selection, and computational scalability....