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What is data science?

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Data science is a multidisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It combines expertise from various domains, including statistics, mathematics, computer science, and domain-specific knowledge, to analyze complex data sets and make informed...
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Data science is a multidisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It combines expertise from various domains, including statistics, mathematics, computer science, and domain-specific knowledge, to analyze complex data sets and make informed decisions. Key components of data science include: Data Collection: Gathering relevant data from various sources, which can include databases, sensors, social media, logs, and more. Data Cleaning and Preprocessing: Cleaning and organizing the data to remove errors, missing values, and inconsistencies. This step is crucial for ensuring the quality of the data. Exploratory Data Analysis (EDA): Exploring the data through statistical methods and visualizations to understand its structure, patterns, and relationships. EDA helps in forming hypotheses and guiding further analysis. Feature Engineering: Creating new features or transforming existing ones to enhance the predictive power of models. Feature engineering involves selecting, combining, or modifying variables to improve model performance. Modeling: Building statistical and machine learning models to make predictions, classifications, or identify patterns within the data. Common algorithms include linear regression, decision trees, support vector machines, and neural networks. Validation and Evaluation: Assessing the performance of models using validation techniques and metrics. This step ensures that models generalize well to new, unseen data. Machine Learning and Predictive Analytics: Using machine learning algorithms to develop predictive models. These models can be applied to forecast future trends, classify data, or make data-driven decisions. Big Data Technologies: Leveraging technologies like Apache Hadoop and Apache Spark to process and analyze large-scale datasets distributed across multiple nodes. Statistical Analysis: Applying statistical methods to draw inferences, test hypotheses, and quantify uncertainty. Statistical analysis is crucial for understanding the reliability of findings. Data Visualization: Creating visual representations of data to communicate findings effectively. Visualization tools help convey complex information in a clear and understandable manner. Natural Language Processing (NLP): Analyzing and interpreting human language through algorithms and computational linguistics. NLP is essential for processing and understanding unstructured text data. Deep Learning: Utilizing neural networks with multiple layers to extract complex patterns and representations from data. Deep learning has proven effective in tasks such as image recognition, speech recognition, and natural language processing. Data science is widely applied across various industries, including finance, healthcare, marketing, and technology, to derive actionable insights, improve decision-making, and solve complex problems. As technology and data continue to evolve, data science remains a dynamic and essential field for extracting meaningful information from the vast amounts of data generated in today's digital age. read less
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I have 2+ yrs working experience in BI domain. Can I pursue Data science for a job change? Will I get Job opportunity as per my experience or not in field of data science? R or python what to chose?
Hi Asish you can choose R or Python selecting programming tools is not criteria learning Deep Analytics is most important you should focus on Mathematicsfor (classification algorithms) statistics(EDA...
Asish
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which is the best college or institute for Data analysis course certificate  with Fresher placement support  in pune?

Hi.. There are the institutes conducting online courses. Like for example, Simplilearn Edureka. Particularly in pune, ExcelR* Hope it will helpful. *before joining compare with other institutes.
Priya
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I want to learn data science in home itself bcz i dont want much time to take any coaching and also most of the institutes are asking high amount for  training. Pease lemme know how i can prepare myself.

First of all you start leaning following. 1.Database(Sql,Nosql) 2 Python,Pandas,Numpy 3 Basic Linux,Big Data(Hadoop,Scala,Spark) 4. Machine Learning 5. Deep Learning
Vishal
What background is required for data science?
Data scientists typically need at least a bachelor's degree in computer science, data science, or a related field. However, many employers in this field prefer a master's degree in data science or a related...
Shivani
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