## 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

- IT professionals looking for career or technology change to Data Science & Analytics.
- BPO industry or non technical professionals who are looking for career in Data Science & Analytics.
- Engineering graduates who want to build a career in Data Science & Analytics.
- Non- technical & MBA graduates who are interested in building a career in Data Science.
- Anyone who likes manipulating data & loves getting insights out of data.
- 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**