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SCOPE OF DATA SCIENCE

Data Science is one of the fastest evolving fields & a Data Scientist’s job is one of the fastest growing and highest paid in tech.

As there is a cut-throat competition in the market, top organizations are turning their minds to data analytics to identify new market opportunities to design their services and products. Surveys show that 75% of top organizations consider data analytics an essential component of business performance. This is where data scientists come in. Data scientists know how to use their skills in math, statistics, programming, and other related subjects to organize large data sets. Then, they apply their knowledge to uncover solutions hidden in the data to take on business challenges and goals. They are thus able to contribute to their organization’s business goals. So, learning data science through effective training can give you a bright future.

“Data Scientist” has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! It is a rewarding career that allows you to solve some of the world’s most interesting problems!

As per Payscale.com, a Data Scientist (IT) with Big Data Analytics skills earns an average salary of Rs 706,750 per year in India.

COURSE OBJECTIVE

Skill Venue is delighted to offer classroom program for candidates to help them build a career in the blooming field of Data Science.The program focuses on building foundation skills in business analytics and data science with in depth training of statistical platform & tools.

We will develop candidates to think analytically and solve business problems using data.
Our innovative approach to analytics training by combining deep knowledge and collaborative learning environment will help candidates to develop real skills in analytics.

Candidates taking this course can expect to gain the knowledge of analytical techniques required by organizations to take strategic decisions, and by solving case studies they will know how analytical techniques can be used to get real insights from real data. Candidates will be able to forecast sales, Identify customer segments, identify drivers of sales/profit, perform regression, analyze customer comments and do much more after the course.

In-depth course coverage, hands-on experience of entire data analytics project cycle, and case studies on real world analytics problems, cutting across different domains are the high points that make Skill Venue’s Data Science in R certification course a leap towards a successful analytics career.

 

WHO SHOULD TAKE THIS COURSE

  1. IT professionals looking for career or technology change to  Data Science & Analytics.
  2. BPO industry  or non technical  professionals who are looking for career in Data Science & Analytics.
  3. Engineering graduates who want to build a career in Data Science & Analytics.
  4. Non- technical & MBA graduates who are interested in building a career in Data Science.
  5. Anyone who likes manipulating data & loves getting insights out of data.
  6. Prerequisite – There are not prerequisite for this course except hard work & dedication.

 

 

Course Curriculum

Lesson 00 - Course Overview
0.1 Course Overview 00:00:00
Lesson 01 - Data Science Overview
1.1 Introduction to Data Science 00:00:00
1.2 Different Sectors Using Data Science 00:00:00
1.3 Purpose and Components of Python 00:00:00
1.4 Quiz 00:00:00
1.5 Key Takeaways 00:00:00
Lesson 02 - Data Analytics Overview
2.2 Knowledge Check 00:00:00
2.1 Data Analytics Process 00:00:00
2.3 Exploratory Data Analysis(EDA) 00:00:00
2.4 EDA-Quantitative Technique 00:00:00
2.5 EDA – Graphical Technique 00:00:00
2.6 Data Analytics Conclusion or Predictions 00:00:00
2.7 Data Analytics Communication 00:00:00
2.8 Data Types for Plotting 00:00:00
2.9 Data Types and Plotting 00:00:00
2.10 Knowledge Check 00:00:00
2.11 Quiz 00:00:00
2.12 Key Takeaways 00:00:00
Lesson 03 - Statistical Analysis and Business Applications
3.1 Introduction to Statistics 00:00:00
3.2 Statistical and Non-statistical Analysis 00:00:00
3.3 Major Categories of Statistics 00:00:00
3.4 Statistical Analysis Considerations 00:00:00
3.5 Population and Sample 00:00:00
3.6 Statistical Analysis Process 00:00:00
3.7 Data Distribution 00:00:00
3.8 Dispersion 00:00:00
3.9 Knowledge Check 00:00:00
3.10 Histogram 00:00:00
3.11 Knowledge Check 00:00:00
3.12 Testing 00:00:00
3.13 Knowledge Check 00:00:00
3.14 Correlation and Inferential Statistics 00:00:00
3.15 Quiz 00:00:00
3.16 Key Takeaways 00:00:00
Lesson 04 - Python Environment Setup and Essentials
4.1 Anaconda 00:00:00
4.2 Installation of Anaconda Python Distribution (contd.) 00:00:00
4.3 Data Types with Python 00:00:00
4.4 Basic Operators and Functions 00:00:00
4.5 Quiz 00:00:00
4.6 Key Takeaways 00:00:00
Lesson 05 - Mathematical Computing with Python (NumPy)
5.1 Introduction to Numpy 00:00:00
5.2 Activity-Sequence it Right 00:00:00
5.3 Demo 01-Creating and Printing an ndarray 00:00:00
5.4 Knowledge Check 00:00:00
5.5 Class and Attributes of ndarray 00:00:00
5.6 Basic Operations 00:00:00
5.7 Activity-Slice It 00:00:00
5.8 Copy and Views 00:00:00
5.9 Mathematical Functions of Numpy 00:00:00
5.10 Assignment 01 00:00:00
5.11 Assignment 01 Solution 00:00:00
5.12 Assignment 02 00:00:00
5.13 Assignment 02 Solution 00:00:00
5.14 Quiz 00:00:00
5.15 Key Takeaways 00:00:00
Lesson 06 - Scientific computing with Python (Scipy)
6.1 Introduction to SciPy 00:00:00
6.2 SciPy Sub Package – Integration and Optimization 00:00:00
6.3 Knowledge Check 00:00:00
6.4 SciPy sub package 00:00:00
6.5 Demo – Calculate Eigenvalues and Eigenvector 00:00:00
6.6 Knowledge Check 00:00:00
6.7 SciPy Sub Package – Statistics, Weave and IO 00:00:00
6.8 Assignment 01 00:00:00
6.9 Assignment 01 Solution 00:00:00
6.10 Assignment 02 00:00:00
6.11 Assignment 02 Solution 00:00:00
6.12 Quiz 00:00:00
6.13 Key Takeaways 00:00:00
Lesson 07 - Data Manipulation with Pandas
7.1 Introduction to Pandas 00:00:00
7.2 Knowledge Check 00:00:00
7.3 Understanding DataFrame 00:00:00
7.4 View and Select Data Demo 00:00:00
7.5 Missing Values 00:00:00
7.6 Data Operations 00:00:00
7.7 Knowledge Check 00:00:00
7.8 File Read and Write Support 00:00:00
7.9 Knowledge Check-Sequence it Right 00:00:00
7.10 Pandas Sql Operation 00:00:00
7.11 Assignment 01 00:00:00
7.12 Assignment 01 Solution 00:00:00
7.13 Assignment 02 00:00:00
7.14 Assignment 02 Solution 00:00:00
7.15 Quiz 00:00:00
7.16 Key Takeaways 00:00:00
Lesson 08 - Machine Learning with Scikit–Learn
8.1 Machine Learning Approach 00:00:00
8.2 Steps 1 and 2 00:00:00
8.3 Steps 3 and 4 00:00:00
8.4 How it Works 00:00:00
8.5 Steps 5 and 6 00:00:00
8.6 Supervised Learning Model Considerations 00:00:00
8.7 Knowledge Check 00:00:00
8.8 Scikit-Learn 00:00:00
8.9 Knowledge Check 00:00:00
8.10 Supervised Learning Models – Linear Regression 00:00:00
8.11 Supervised Learning Models – Logistic Regression 00:00:00
8.12 Unsupervised Learning Models 00:00:00
8.13 Pipeline 00:00:00
8.14 Model Persistence and Evaluation 00:00:00
8.15 Knowledge Check 00:00:00
8.16 Assignment 01 00:00:00
8.17 Assignment 01 Solution 00:00:00
8.18 Assignment 02 00:00:00
8.19 Assignment 02 Solution 00:00:00
8.20 Quiz 00:00:00
8.21 Key Takeaways 00:00:00
Lesson 09 - Natural Language Processing with Scikit Learn
9.1 NLP Overview 00:00:00
9.2 NLP Applications 00:00:00
9.3 Knowledge check 00:00:00
9.4 NLP Libraries-Scikit 00:00:00
9.5 Extraction Considerations 00:00:00
9.6 Scikit Learn-Model Training and Grid Search 00:00:00
9.7 Assignment 01 00:00:00
9.8 Assignment 01 Solution 00:00:00
9.9 Assignment 02 00:00:00
9.10 Assignment 02 Solution 00:00:00
9.11 Quiz 00:00:00
9.12 Key Takeaway 00:00:00
Lesson 10 - Data Visualization in Python using matplotlib
10.1 Introduction to Data Visualization 00:00:00
10.2 Knowledge Check 00:00:00
10.3 Line Properties 00:00:00
10.4 (x,y) Plot and Subplots 00:00:00
10.5 Knowledge Check 00:00:00
10.6 Types of Plots 00:00:00
10.7 Assignment 01 00:00:00
10.8 Assignment 01 Solution 00:00:00
10.9 Assignment 02 00:00:00
10.10 Assignment 02 Solution 00:00:00
10.11 Quiz 00:00:00
10.12 Key Takeaways 00:00:00
Lesson 11 - Web Scraping with BeautifulSoup
11.1 Web Scraping and Parsing 00:00:00
11.2 Knowledge Check 00:00:00
11.3 Understanding and Searching the Tree 00:00:00
11.4 Navigating options 00:00:00
11.5 Demo3 Navigating a Tree 00:00:00
11.6 Knowledge Check 00:00:00
11.7 Modifying the Tree 00:00:00
11.8 Parsing and Printing the Document 00:00:00
11.9 Assignment 01 00:00:00
11.10 Assignment 01 Solution 00:00:00
11.11 Assignment 02 00:00:00
11.12 Assignment 02 Solution 00:00:00
11.13 Quiz 00:00:00
11.14 Key takeaways 00:00:00
Lesson 12 - Python integration with Hadoop MapReduce and Spark
12.1 Why Big Data Solutions are Provided for Python 00:00:00
12.2 Hadoop Core Components 00:00:00
12.3 Python Integration with HDFS using Hadoop Streaming 00:00:00
12.4 Demo 01 – Using Hadoop Streaming for Calculating Word Count 00:00:00
12.5 Knowledge Check 00:00:00
12.6 Python Integration with Spark using PySpark 00:00:00
12.7 Demo 02 – Using PySpark to Determine Word Count 00:00:00
12.8 Knowledge Check 00:00:00
12.9 Assignment 01 00:00:00
12.10 Assignment 01 Solution 00:00:00
12.11 Assignment 02 00:00:00
12.12 Assignment 02 Solution 00:00:00
12.13 Quiz 00:00:00
12.14 Key takeaways 00:00:00
Project 01
Project 1 Stock Market Data Analysis 00:00:00
Project 02
Project 2 Predict if a loan will get approved or not. 00:00:00
Project 03
Predict the sales for an ecommerce company 00:00:00
0 STUDENTS ENROLLED

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