What you have to know about our training before proceeding further with SAM Career Center:

* For more than 25 years we successfully trained thousands of people;
* We are located in the most vibrant and advanced business area of the World - New York City;
* All our instructors are active working professionals in the areas of their expertise;
* Due to the current situation, all our Classroom training is conducting in a Virtual mode (Remotely);
* Free professional counseling will be provided before any formal enrollment;
* Our only goal is for our Hands-On training program to have you ready to work right after the training;
* You can retake any program you just learned for free, which is one of our unique policies;

COURSE DESCRIPTION:
This unique comprehensive course provides solid knowledge in Data Analysis and Statistical Software. Through a combination of lectures and exercises, students will learn the key techniques and approaches necessary to be an effective Data Analyst. Students will learn fundamentals of Data Analysis with Python programming, advanced Excel techniques and VBA macros, basics of SQL Server Databases, Data Visualization with Tableau and Power BI. After completing this course, students will be able to work as a Data Analyst or perform related work that requires knowledge of data analysis, data manipulation and data visualization. This training is available in-class and remotely (online).

COURSE CONTENT:

1. Statistics Basics – students will review basic concepts and terminology in Statistics.

2. Python Programming Fundamentals – the module includes following topics: Basic Data Types (Basic Data Types, Variables, Operators, Functions and Modules), Compound Data Types (Lists, Strings, Sets, Dictionaries), Flow control (Conditional expressions, Loops, Iterators), Working with files, Working with functions, OOP Concepts, Benefits of Standard Library.

3. Data Analysis and Visualization with Python – module provides solid fundamentals of Data Analysis and Data Visualization using functions and Python Libraries, such as Numpy, Pandas, Seaborn etc.

4. Machine Learning Fundamentals - this module will introduce Machine Learning and Data Mining with Python. Students will learn predictive analytics - supervised and unsupervised learning. The following topics will be covered: Linear regression, Logistic regression, Train/Validation, Data Visualization, Model Performance Evaluation, Classification Trees, Ensemble Learning, Random Forests, Gradient Boosting, Neural Networks, Clustering (K-means), Dimensionality Reduction (PCA), Text mining (NLTK), Association Rule Mining and Basket analysis. Student will be able to build their projects portfolio and post it on GitHub.

5. Data Analysis with Excel – in this module students will learn Excel advanced techniques, including Math Functions, Logical Functions, Statistical Functions, Lookup, Sort/Filter Data, Pivot Tables and Pivot Charts, Power Pivot Tables, Data Analysis Tools etc. Also, this module provides introduction to VBA scripting and using Macros.

6. SQL Databases Fundamentals – this module provides solid fundamentals of SQL databases, with emphasis on querying data. Students will learn how to filter, group and sort data, retrieve data from multiple tables using joins and unions, built-in functions to manipulate dates and strings, primary and foreign key table relationships, understand indexes for performance gains. The following topics will be covered: select queries, filtering, grouping, sorting data, combining tables with joins and union, understanding primary and foreign key relationship, views, built in system functions for querying data, conditional testing with If and Case statements, looping, inserting, updating and deleting data basics.

7. Data Visualization with Tableau – the module includes: using the Tableau interface/paradigm to create data visualizations, creating calculations, building Dashboards, advanced chart types and visualization, complex calculations to manipulate data, use statistical techniques to analyze data, implement advanced geographic mapping techniques and visualizations of non-geographic data, prep data for analysis, combine data sources using data blending.


200 instructor-led in-class academic hours, plus labs

Prerequisites:
Basics of Statistics and Excel