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What is Data Science Course?
Data science Training is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured. Data science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, f which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions. In this data science course, you will deal with both structured and unstructured data. The algorithms also involve predictive analytics in them.
Thus, data science is all about the present and future. That is, finding out the trends based on historical data which can be useful for present decisions and finding patterns which can be modelled and can be used for predictions to see what things may look like in the future. The Data Science course is an amalgamation of Statistics, Tools, and Business knowledge. So, it becomes imperative for a Data Scientist to have good knowledge and understanding of these.
Why Data science Courses is important?
Data is meaningless until its conversion into valuable information. Data Science course involves mining large datasets containing structured and unstructured data and identifying hidden patterns to extract actionable insights. The importance of Data Science lies in its innumerable uses that range from daily activities like asking Siri or Alexa for recommendations to more complex applications like operating a self-driving car.
What you will learn in Data science Training ?
The Introduction to Data Science class will survey the foundational topics in data science, namely: Data Manipulation. Data Analysis with Statistics and Machine Learning. Data Communication with Information Visualization. Future Vision Computers has designed best course for data science training for machine learning.
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Curriculum
- 21 Sections
- 74 Lessons
- 24 Weeks
- HYPOTHESIS TESTING5
- MODEL SELCTION CASE STUDY3
- TIME SERIES FORECASTING4
- MODEL SELECTION & GENERAL ML TECHNIQUES3
- ADVANCED ML CASE STUDY4
- ADVANCED REGRESSION2
- PRINCIPAL COMPONENT ANALYSIS2
- BOOSTING2
- BAGGING & RANDOM FOREST5
- CASE STUDY: LEAD SCORING4
- BUSINESS PROBLEM SOLVING2
- BASICS OF NLP AND TEXT MINING4
- UNSUPERVISED LEARNING: CLUSTERING4
- CLASSIFICATION USING DECISION TREES3
- LOGISTIC REGRESSION4
- LINEAR REGRESSION5
- DATA ANALYSIS USING SQL6
- EXPLORATORY DATA ANALYSIS4
- ANALYTICS PROBLEM SOLVING2
- PYTHON FOR DATA SCIENCE4
- DATA VISUALIZATION IN PYTHON2
Siddharth Parakh

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