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Slide 1 - What is Data Science? Employing concepts like statistical analysis, data analysis, machine learning techniques, data modeling, data preparation, etc., to extract usable information from unstructured data. How Does Data Science Work? The following can be used to describe how data science functions: Raw data that illustrates the business issue is acquired from many sources. Data modeling is carried out using a variety of statistical analysis and machine learning techniques to find the best solutions that adequately explain the business problem. Actionable insights that will help solve the business issues identified by data science.
Slide 2 - Data Science Life Cycle The following steps are included in the data science lifecycle: Formulating a Business Problem Any problem with data science will start with the definition of a business problem. The challenges that might be resolved with knowledge obtained from a successful data science solution are explained by a business problem. For a retail store, you have sales information dating back a year. This is an easy-to-understand illustration of a business dilemma. You must predict or forecast the store's sales over the next three months using machine learning techniques in order to enable the retailer to build an inventory that will minimize the loss of products having shorter shelf life than other products. Data Extraction, Transformation, Loading The creation of a data pipeline is the next phase in the data science life cycle. In this step, the pertinent data is taken from the source, translated into machine-readable format, and then loaded into the program or machine learning pipeline to get things going. For the aforementioned scenario, we will need data from the shop that will be helpful in creating an effective machine learning model in order to estimate the sales. As a result, we would produce several data points that might or might not affect the sales for that particular store. Data Preprocessing The magic happens in the third phase. Utilizing statistical analysis, exploratory analysis, data wrangling, and data manipulation, we will generate pertinent data. Preprocessing is carried out to evaluate the different data points and create hypotheses that best explain the relationship between the different elements in the data.
Slide 3 - For instance, the data must be in a time series format in order to estimate retail sales. The series' stationarity will be examined by hypothesis testing, and further calculations will reveal numerous trends, seasonality, and other relationship patterns in the data. Data Modeling Advanced machine learning techniques are employed in this step to select features, convert features, standardize the data, normalize the data, etc. You can build a model that will effectively produce a forecast for the specified months in the example above by selecting the best algorithms based on evidence from the aforementioned phases. For a business challenge where high dimensional data may be present, we can use the time series forecasting approach. We'll create a forecasting model utilizing an AR, MA, or ARIMA model and other dimensionality reduction approaches to predict the sales for the upcoming quarter. Gathering Actionable Insights Getting insights from the aforementioned problem description is the last stage of the data science life cycle. From the entire process, we derive conclusions and results that most effectively explain the business issue. For instance, we can obtain the upcoming three months' worth of monthly or weekly sales from the time series model mentioned above. The experts will then be able to develop a strategic plan using these insights to solve the current issue. Solutions to Business Problems The only things that will address the business challenge are practical insights supported by data and actionable insights. As an illustration, our projection based on the time series model will provide a reliable estimation of the shop sales for the next three months. The store can plan its inventory using such data to minimize the loss of perishable goods.
Slide 4 - Requirements for data science A number of conditions must be met in order to effectively implement data science solutions in a company. The following are some requirements: Programming Knowledge Professionals must be knowledgeable in programming languages like Python or R to perform the statistical calculations and calculations needed for data science operations. You can easily build machine learning models from scratch with the assistance of libraries and scripting experience. Some of the built-in Python programming libraries that can be used for data science with Python are Scikit-learn, Tensorflow, pandas, matplotlib, seaborn, scipy, numpy, etc. Statistics, Probability, And Linear Algebra If you are serious about pursuing a career in data science, you must possess an understanding of both descriptive and inferential statistics. With the aid of statistical analysis, you can draw a variety of conclusions and comprehend the data at hand. One illustration would be how we talked about using hypothesis testing to determine whether or not a time series is stationary. Understanding complicated machine learning algorithms is significantly shaped by probability and linear algebra. Knowing these ideas will make it simpler for you to comprehend how different machine learning algorithms operate internally. SQL, Excel And Visualization Tools The interactive interfaces that visualization tools like PowerBI, Tableau, etc. can offer to depict different data points are excellent and can aid in completing preliminary analysis or simply help one to understand the data.
Slide 5 - On the other hand, SQL and Excel can aid in your understanding of how data is represented in tabular format or data frames that aid in data wrangling and manipulation. Big Data And Cloud The cloud enters the scene when a machine learning model is deployed at scale in order to be able to magnify the outcomes and learning for any business problem. We use machine learning on the cloud for. And big data offers a better view on how to handle massive and complicated data for our business problems as well as for building data pipelines for the scaled-up continuous development of different machine learning models. Roles and Responsibilities of a Data Scientist We can categorize data scientists in the following way depending on how different their roles and duties are from company to organization. Any organization's data scientist will be responsible for the following: Data Extraction, Loading, Transformation Exploratory Data Analysis Data Manipulation Statistical Analysis Visualization Data Modeling Gathering Actionable Insights Why Data Science? Right now, every industry is in desperate need of qualified data scientists. They are among the IT industry's highest-paid employees.They rank among the best-paid workers in the IT sector. A data scientist earns an average pay of $110,000 per year, making it the highest paid profession in America, according
Slide 6 - to Glassdoor. Few people possess the capacity to draw valuable conclusions from raw facts. This information is compiled from all available sources, including Information about customers is collected by sensors in malls. Facebook and other social media posts Smartphones can capture digital photos and videos. E-commerce transactions for purchases This data is known as big data. Organizations and corporations are continually being inundated with enormous amounts of data. Therefore, it is essential to know how to use this data and what to do with it. In the graphic above, data science is conceptualised. It brings together a range of skills, such as statistics, math, and business domain expertise, and helps firms with: Cut expenses enter brand-new markets Utilize various demographics evaluate the success of marketing initiatives Introduce fresh goods or services
 

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