Data Science

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Course overview

Course Description

This course is designed to teach Data Science in a hands-on manner and prepare participants for a career in this field. The course will provide you with the complete toolbox to become a Data Scientist. Students will acquire the precise technical skills recruiters are looking for when hiring Data Scientists.

On completion, students will have gained the analytical skills required to open the doors to a lucrative career as a Data Scientist.

Data is extremely important to all organizations, and at all levels. It's not just big IT and software companies: Data experts are needed in banking and finance, automotive, energy, healthcare, transport, retail, and virtually every domain you can think of. And because data drives decisions - from small regional offices to the boardroom - graduates from bootcamps in Data Science will be directly involved in important strategic decision-making processes.

The role of data scientist is now a buzz worthy career. It has staying power in the marketplace and provides opportunities for people who study data science to make valuable contributions to their companies and societies at large. LinkedIn recently picked data scientist as its most promising career of 2019. One of the reasons it got the top spot was that the average salary for people in the role is $130,000.

Data science is one of the fastest-growing sectors of the tech industry. In simple words, there is soaring demand for Data professionals yet a huge deficit on the supply side. The course will qualify you for a position as a data scientist or a data analyst. This program will ensure you have the knowledge to kick-start your career in Data Science.

Certificate

On successful completion of the course participants will be awarded participation certificate from DigiPAKISTAN. Also prepare for International Exam.

Duration & Frequency

Total Duration of the course is 3 months

What will i learn?

The course provides the entire toolbox you need to become a data scientist
Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
Start coding in Python and learn how to use it for statistical analysis
Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
Unfold the power of deep neural networks
Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Requirements

No prior experience is required. We will start from the very basics
You’ll need to install Anaconda. We will show you how to do that step by step
Microsoft Excel 2003, 2010, 2013, 2016, or 365

Curriculum for this course

Data Science Certification Introduction

Data Science Certification Introduction of Course

Data Science Intro & Python Data Types

DS-Jupyter Notebook Installation and setup of anaconda
DS-Data Science Confusion terms
DS- Data Analytics, Big Data Analytics and Data Science
DS- Data Science Definition and Steps
DS- Data Types Numbers and Strings python Language Hands on
DS- Data Types List, set and Tuple
DS- Data Types Dictionay

Advance Python Learning

DS-Python Condition and While Loop
DS-For loops
DS- Classes ,Input statement
DS-Function, Lambda function with map and reduce
DS- List Comprehension and quiz discussion

Data Engeerining

DS-Overview of Data Engeering
DS-Types of Data
DS- Data Science Stages
DS- Handling of missing and noisy data
DS Data Normalization and Discritizaiation

Data Engineering NUMPY and PANDAS

DS- Numpy Arrays
DS-Numpy Array operation
DS-Pandas Series
DS-Pandas Dataframe
DS- Pandas Missing valuses Dropna and filling
DS- Pandas Groupby
DS-Pandas concat, merge, join
DS- Dataframe Input and output
DS-Dataframe pandas operations
DS- Pandas Case studies examples
DS-Pandas Assignment explnation of 2 and 3

Data Exploration

DS-Overview of Data Exploration
DS-Overview of Libraries
DS-Seaborn Categorical data plots
DS-Matplotlib Practice
DS-Seaborn Distribution plots
DS-Seaborn Matrix plots
DS-Assignment overview

Machine Learning Intro and LInear Regression

DS-Machine Learning introduction
DS-Machine Learning Supervised and Unsupervised Learning
DS- Linear Regression Concepts
DS- Linear Regression Aanalysis Model Goodness
DS-Linear Regression Hands-on Case Study of House Price

ML Logistic Regression and classfication Metrics

DS-Logistic regression concept
DS-Logistic regression hands-on till Data EDA-I
DS-Logistic regression hands-on case Study -II
DS-Classfication Metrics
DS-Assignment demo

Data Science Statistics

DS-Receivier Operating Characteristics (AUC) Metrics page 4 to 6
DS-Data Science Statistics ( Population , sampling ,mean , median, standard deviation , Distribuations)
DS- Varience Bias, Standard dev
DS-Covarience