Description:
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. It is a combination of mathematics, programming, data engineering, and subject matter expertise, used to analyze and interpret data for decision-making and problem-solving.
It encompasses a variety of topics like machine learning (ML), artificial intelligence (AI), natural language processing, data mining, and data visualization. Data scientists use a variety of tools and techniques to extract and analyze data from different sources, such as databases, spreadsheets, and other data sources. These insights are then used to develop models, algorithms, and other predictive models to solve problems and make decisions. Data science is an interdisciplinary field that incorporates elements of computer science, mathematics, statistics, and other fields.
Why Is Data Science Important?
Data science is an increasingly important field in our modern world. It is used to analyze large amounts of data in order to gain useful insights and make informed decisions. Data science is used in a variety of industries, from finance and healthcare to marketing and retail. It is used to identify trends, optimize processes, and create predictive models that can help businesses and organizations make better decisions.
Data science is essential to the success of any business or organization, and its importance is only going to increase in the years ahead. Becoming a data scientist equips you with essential skills for success in the industry and makes you a valuable asset in any organization you find yourself.
Add Video Here: Introduction to machine learning
Important Skills/Subjects that a Data Scientist Must Be Equipped With
1. Programming Languages: A data scientist should be proficient in at least one programming language such as Python, R, Java, or Scala.
2. Machine Learning: Data scientists must have a deep understanding of machine learning algorithms and techniques.
3. Statistics: Knowledge of basic statistics, probability theory, and linear algebra is essential for a data scientist.
4. Data Analysis: A data scientist must be able to analyze large amounts of data and draw meaningful insights from it.
5. Data Visualization: Data scientists must be able to create visualizations and graphs to communicate their findings to stakeholders.
6. Cloud Computing: Experience with cloud computing platforms such as Amazon Web Services and Microsoft Azure is increasingly important for data scientists.
7. Database Management: Knowledge of database management systems such as SQL and NoSQL is essential for a data scientist.
8. Business Acumen: Data scientists must understand the business context and be able to work with stakeholders to provide meaningful insights.
The flow of A Data Science Project
To start with any data science project, you must follow the steps given below:
- Understand the Business Problem: Identify the project goal, target audience, and data needed.
- Acquire the Data: Collect the data from the appropriate sources.
- Clean and Explore the Data: Clean the data from any anomalies and explore the data to gain insights.
- Build the Model: Build and tune different models to solve the problem.
- Collect Insight: Evaluate the performance of the models and interpret the results.
- Deployment: Deploy the model in a production environment.
What is Machine Learning?
Machine learning is a subset of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The learning process begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
What Are ML Algorithms?
Machine learning algorithms are computer algorithms that learn from data and improve their performance over time without being explicitly programmed. These algorithms can identify patterns in data, detect anomalies, and make predictions. Examples of ML algorithms include decision trees, random forests, support vector machines, and neural networks.
Step for Data Preprocessing
Binarization
Binarization is the process of converting data into a binary format. This is usually done to make data easier to store and process by computers. Binarization involves converting numerical or categorical data into a binary format in which each value is represented as either a 0 or a 1. For instance, a categorical variable with three possible values (e.g., high, medium, low) might be converted into a binary format that would represent each value as either 0 (low), 1 (medium), or 2 (high).
Step 1. Import Num library as a name “np” and import preprocessing function from sklearn.
Step 2. Create a NumPy array for sample data.
Step 3. Binarization Output
To binarize the data, use preprocessing.Binaries(). This function binarizes data according to an imposed threshold. Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1. In our case, the threshold imposed is 1.4, so values greater than 1.4 are mapped to 1, while values less than 1.4 are mapped to 0.
Output:
(II) Standard Deviation: Standard Deviation is a measure of the spread or dispersion of a set of data values. It is calculated as the square root of the variance of the data. It is used to measure how much variation or dispersion exists from the average (mean) of the data set.
Step 4. Now, find out the mean and standard deviation value for input data.
Output:
Mean removal technique
Mean Removal is a data preprocessing technique in Data Science which involves subtracting the mean value from each data point in a dataset. This is done to centre all the data around zero, which can be useful when training a model. The mean value is calculated by taking the average of all data points in the dataset. This technique is useful for removing the mean from our feature vector so that each feature is centered on zero. This is done to remove bias from the features in the feature vector.
Step 5. To do mean removal in input data, use the use scale function.
Output:
Step 6. Now map or scale the values according to the minimum and maximum values of the input data.
Scaling
Data Scaling is a process used to normalize the range of independent variables in order to make sure that the data points can be treated equally. It is also used to bring all the data points to the same level of magnitude which helps in faster processing and better performance of the data models. Scaling helps in many ways such as it avoids bias, improves accuracy, improves convergence speed when training a model, and also makes it easier to compare different data points.
Output:
Step 7. Now, use normalization technique to modify the values in the feature vector so that we can measure them on a common scale. In Machine learning, we use many different forms of normalization. Some of the most common forms of normalization aim to modify the values so that they sum up to 1.
Label Encoding: When we perform classification, we usually deal with a lot of labels. These labels can be in the form of words, numbers, or something else. The machine learning functions in sklearn expect them to be numbered. So, if they are already numbers, we can use them directly to start training. But this is not usually the case.
Step 8: Create one list for colors with input_labels variable.
Step 9. Now, map labels into numerical values according to order.
Output:
Step 10. Encode labels values using encoder.transform function.
Output:
Step 11. Now, inverse these encoded values to label.
Output:
Conclusion:
This tutorial has covered the basics of data pre-processing, from understanding the need for pre-processing to the actual steps in the process. We also discussed some of the common techniques used in data pre-processing. Additionally, this tutorial has discussed the importance of data validation and the process of splitting data into training, validation, and testing sets. By following the steps outlined in this tutorial, data scientists can ensure that their data is clean, accurate, and ready for analysis.
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