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Are you stuck with your assignment? Are you looking for help in Data Science Assignment Help? Are you looking for an expert who can help in your assignment? We have an expert team of Data science professionals who would be available to work on Data Science Assignment. Our team will understand the requirements and will complete the assignment flawlessly and plagiarism free. Our expert will assure you that you will provide the best solutions for your assignment. Our Data Science Assignment help experts will write the assignment according to the requirement given by the professor and by thoroughly following the university guidelines. Our expert will help you secure higher grades in the examination. We will complete the assignment before the time span with the best solution. Our Data Science Assignment help expert will provide the proper guidance and complete solution for your assignment.
What is Data Science ?
Data science is one of the most promising and in demand career paths for skilled professionals. There are many combined fields in data science. It includes Artificial intelligence, data analysis, statistics, scientific methods which extract meaningful information from the structured and unstructured dataset and apply knowledge and actionable insights from data across a broad range of application domains. It is related to data mining, machine learning and big data. A data scientist is someone who creates programming code, and combines it with statistical knowledge to create insights on business data.
Data science consists of lots of techniques. It involves statistical analysis, scientific methods, process and machine learning algorithms. It is used to extract insights from structure and unstructured data. Data science involves some new technologies such as Data cleaning, data analysis, data mining, statistical analysis, Machine learning, Deep learning etc.
Data Science Process :
Data processing involves various stages such as data acquisition, data mining, data cleaning, Exploratory data analysis.
We provide the data science assignment help on following topics :
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Supervised and unsupervised learning
Natural Language Processing
Concept of Data science projects
Data cleaning : Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data
Data analysis : Data analysis is a process of inspecting,cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
Data visualization : Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
Predictive analytics : Predictive analytics is most commonly used in statistical analysis. It is under the statistics, machine learning and optimization techniques. It predicts the future data by analyzing current and historical data. Predictive analytics process consist of the following steps :
Data analysis and messaging
statistical and machine learning
Prediction and monitoring
Prescriptive analytics : The prescriptive analytics focus on how to take advantage of future risk and implication of each decision. The prescriptive analytics possess uncertainty and complexity. The absence of prescriptive analytics in reality necessitates a number of studies to integrate prediction models, optimization approaches, domain expertise, and decision-making procedures.
Machine learning : Machine learning is the branch of artificial intelligence that enables a system to learn from data rather than through explicit programming. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes.
Deep Learning : Deep learning is the sub part of machine learning. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance
Natural Language processing : Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.
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