MASTER DEGREE PROGRAMIN DATA SCIENCE
Data field is evolving each day and new opportunities are coming everyday. Keeping this in mind we have designed a Master Program in Data science under the exceptional mentorship of leading data scientists from the data fraternity. For 6 months you will be learning, practicing data science with Capstone projects and hands on practical exposure. After the end of cohort, you will be working as data scientist as Full time candidature or as a Trainee with some leading data science company.
Key features of SkillCircle’s Master Degree In Data Science:
- 6 Months Advanced Training
- Modules with certifications
- Placement Assistance
- Career guidance and mentorship by Skill Circle and industry leaders.
- Resume Review and interview preparation .
- Access to Opportunities with leading companies.
- 8 live Projects
- Real-world case studies to build practical skills.
- Hands – on exposure to analytics tools & techniques such as Python, Tableau, SQL
- Learn industry insights through multiple industry knowledge sessions.
Why Choose Data Science Course?
- Growing demand for Data Science professionals.
- Data Science has a wide application across various industries.
- High paying jobs.
- It plays a key role of valued addition for any business.
- Due to Skill Gap it is easy to grab a job in Data Science field.
Companies Hiring our Candidates :
CRED, HSBC, KOTAK BANK, Razor Pay, Policy Bazaar, Oyo Rooms, TATA IQ, CRED, KPMG, Genpact, IBM, Info Gain, UBER, Renault , Accenture, Bewakoof.com, Swiggy, TVS, Mahindra, Paisa Bazaar
INTRODUCTION TO DATA ANALYTICS WITH PYTHON
• What is Analytics & Data Science?
• Common Terms in Analytics
• What is data
• Classification of data
• Relevance in industry and need of the hour
• Types of problems and business objectives in
• How leading companies are harnessing the
power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving
• List of steps in Analytics projects
• Identify the most appropriate solution design
for the given problem statement
• Project plan for Analytics project & key
milestones based on effort estimates
• Build Resource plan for analytics project
• Why Python for data science?
• Introduction to python and comparison with
other programming language
• Installation of Anaconda Distribution and other
Python Objects ,Numbers & Booleans, Strings,
Container objects, Mutability of objects
• Concept of Packages/Libraries – Important
• Operators – Arithmetic, Bitwise, Comparison and
• Conditions (If else, if-elif)
STRING OBJECTS AND COLLECTION
* String Object Basics
* String Methods
* Splitting and joining strings
* String Format Functions
*List object Basics
TUPLES, SET, DICTIONARIES & FUNCTIONS
* Tuples, Sets, Dictionary object basics,
Dictionary, Object methods, Dictionary
* Functions basics, Iterators
* Generator functions
*Map, Reduce & filter functions
OOPS CONCEPTS & WORKING WITH FILES
•OOPS basic concepts
*Creating classes and objects
*Working with files
• Reading and Writing files
• Python pandas – series
• Python pandas – Dataframe
• Python pandas – Basic Functionality
• Python pandas- Reindexing
+ Python pandas – Iteration
• Python pandas – Sortings
• Python pandas – Working with Text dala
Critical success drivers
* Python pandas – options and customization
• Python pandas – Indexing and selecting data
• Python pandas – Date Functionality
• Python pandas – Categorical Data
Python pandas – Visulaization
DATA MANIPULATION: CLEANSING – MUNGING
• Cleansing Data with Python
• Filling missing values using lambda function and
concept of Skewness.
• Python Objects ,Numbers & Booleans, Strings,
• Data Manipulation steps
DATA ANALYSIS : VISUALIZATION USING PYTHON
• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and
• Univariate Analysis
• Bivariate Analysis
• creating Graphs
• Important Packages for Exploratory Analysis
INTRODUCTION TO STATISTICS
*Sample vs Population Statistics
•Probability distribution functions
*Central limit theorem
•Spread and Dispersion
•Z-stats vs T-stats
*Type 1 & Type 2 error
*Chi Square Test
*T-test 1-Tail 2-Tail Test
*Correlation and Co-variance
INTRODUCTION TO PREDICTIVE MODELING
*Concept of model in analytics and how it
*Common terminology used in Analytics &
*Popular Modeling algorithms
• Types of Business problems – Mapping of
Different Phases of Predictive Modeling
DATA EXPLORATION FOR MODELING
• Need for structured exploratory data
• EDA framework for exploring the data and
identifying any problems with the
• data by the help of pairplot
• Identify missing data
• Identify outliers data
• Visualize the data trends and patterns
• Normalizing the data
• Feature Engineering
• Feature Selection
• Feature scaling using Standard Scaler
• Label encoding
• Need of Data preparation
• Consolidation/Aggregation – Outlier treatment –
Flat Liners – Missing values-Dummy creation –
• Variable Reduction Techniques – Factor & PCA
•Introduction to Machine Learning
•Linear regression assignment
•Tree Models + Boosting
• Model selection & general ML techniques
*Bagging & Boosting
•CHATBOT case study