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Big Data and Data Sciences Introduction - FREE WRKSHOP ONLINE - Price: $0

Ad # 731300
74300 (map)
March 24, 2020
March 24, 2021
Utilize your FREE time - Join Online classes and FREE workshops
Big Data and Data Sciences Introduction - FREE WORKSHOP ONLINE
[Training Course Karachi Pakistan Dubai ]
Free Workshop 28th Mar, 2020 at 04:00 PM (Location: DHA) 2 hours
Online Classes Available| FREE Practice Exam
💯 Join FREE Workshop & Training
💯 For Course detail
☎️ #Gulshan: 021-3498-6664, #WhatsApp 03122169325 #DHA 0333-3808376, 02135344600
#data_science #big_data #data_sciences_training
Data Sciences Specialist Course
Data science is the study of the extraction of knowledge from data. It uses various techniques from many fields, including signal processing, mathematics, probability, machine learning, computer programming, statistics, data engineering, pattern matching, and data visualization, with the goal of extracting useful knowledge from the data. With computer systems able to handle more data, big data is an important aspect of data science.
A person that does data science is called a data scientist. Data scientists solve complicated data problems using mathematics, statistics and computer science, although very good skill in these subjects are not required. However, a data scientist is most likely to be an expert in only one or two of these disciplines, meaning that cross disciplinary teams can be a key component of data science.
Good data scientists are able to apply their skills to achieve many kinds of purposes. Their skills and competencies vary widely.

Data Sciences Summary
Defining Data Science and What Data Scientists Do
In this module, you will go over the course syllabus to learn what will be taught in this course. Also, you will hear from data science professionals to learn what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will be required to complete a reading assignment to learn why data science is considered the sexiest job in the 21st century.
Data Science in Business
In this module, you will learn about what companies need to do in order to start with data science. You will also learn about some of the qualities that differentiate data scientists from other professionals. In addition, you will learn about analytics and what important role data scientists play in this process, and about story-telling and the importance of an effective final deliverable. Finally, you will be required to apply what you learned about data science by answering open-ended questions.

Module-01 Machine Learning Data Analysis with Python [ Fee : 20,000]
Basic Concepts, History and Evolution of Machine Learning for Big Data as a business application domain. Use cases in different business industries for both small and big data. A review of Machine Learning techniques and algorithms and their interpretation from a business perspective. Theoretical and Practical Exposure of Cluster Analysis and Support Vector Machines.
KEY Learning
 Python Data Analysis with NumPy and Pandas
 Python Data Visualization – Matplotlib and Pandas
 Math of Machine Learning – Probability, Regression, Vectors, Matrices, Baysian, K Nearest
 Machine Learning with Python – Supervised vs Unsupervised Learning and Train / Test
 Data Mining, Big Data with Machine Leaning – Apache Spark
 Neural Networks and Deep Learning.
 Machine Learning Project – Develop Product Recommender System in Python

Module-02 Understanding Data Science & Data Mining [ Fee : 20,000]
In this module, you will learn about data science and what skills are required for anyone interested in pursuing a career in this field and as he gives advice to those who are looking to start a career in data science. Finally, you will be required to complete reading assignments to learn about the process of mining a given dataset and about regression analysis.
Data Science Course Key Learning
 Introduction and Decision Trees, Typical Data Science Tasks, Data Science Applications/Examples
 Prediction techniques Continues: Holdout Method, ROC Curve Interpretation, Artificial Neural Network
 Regression and Ensemble Methods: Linear Regression Analysis, Ensemble Methods (Bagging and Boosting), K-Nearest Neighbors Method
 Clustering: K-Means Clustering, Hierarchal Clustering, Fuzzy C-Means
 Text Analysis: Part of Speech tagging, Bags of Words Creation, Part of Speech and Stop Word Filters, Stemming Text, Term Frequency Count
 Market Basket Analysis of Consumer: Association Rule Mining, Apriori Method, Frequent Item sets

Data Sciences Course Audience
 This course is primarily for individuals who are passionate about the field of data science and who are aspiring to become data scientists.
Data Science Course Benefits
 Individuals who earn this certification were able to describe the Data Science and What Data Scientists Do, how data sciences helping modern business to perform better, understanding data and patterns.
Course Prerequi
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