Skill Degree In Data Science

Data is the new oil, there is no doubt about this fact however to leverage data you need the right skills and exposure working under a mentor who himself is a data
scientist! You’ll master the skills necessary to become a successful Data Scientist. You’ll work on projects designed by industry experts, and learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud..

Key features of SkillCircle’s Skill Degree In Data Science:

• 4 Months Advanced Training
• 14+ 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.
• 10 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 Analytics professionals.
• Data Analytics 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 Analytics 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
various industries
• How leading companies are harnessing the
power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving
framework
• 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?

PYTHON CORE

• Introduction to python and comparison with
other programming language
• Installation of Anaconda Distribution and other
python IDE
Python Objects ,Numbers & Booleans, Strings,
Container objects, Mutability of objects
• Concept of Packages/Libraries – Important
packages
• Operators – Arithmetic, Bitwise, Comparison and
Assignment Operators
• Conditions (If else, if-elif)

STRING OBJECTS AND COLLECTION

* String Object Basics
* String Methods
* Splitting and joining strings
* String Format Functions
*List object Basics
*List Methods

TUPLES, SET, DICTIONARIES & FUNCTIONS
* Tuples, Sets, Dictionary object basics,
Dictionary, Object methods, Dictionary
view objects
* Functions basics, Iterators
* Generator functions
*Lambda Functions
*Map, Reduce & filter functions

OOPS CONCEPTS & WORKING WITH FILES
•OOPS basic concepts
*Creating classes and objects
*Working with files
• Reading and Writing files

DATABASE
*Mongo DB
*Python SQL

PYTHON PANDAS
• 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
summarization
• Univariate Analysis
• Bivariate Analysis
• creating Graphs
• Important Packages for Exploratory Analysis

INTRODUCTION TO STATISTICS
•Descriptive Statistics
*Sample vs Population Statistics
•Random variables
•Probability distribution functions
*Expected value
*Normal distribution
*Gaussian distribution
•Z-score
*Central limit theorem
•Spread and Dispersion
Hypothesis testing
•Z-stats vs T-stats
*Type 1 & Type 2 error
*Confidence Interval
•ANOVA Test
*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
is used?
*Common terminology used in Analytics &
Modeling process
*Popular Modeling algorithms
• Types of Business problems – Mapping of
Techniques
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

DATA PREPARATION
• Merging
• 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
• Variable Reduction Techniques – Factor & PCA
Analysis

ARTIFICIAL INTELLIGENCE

•Introduction to Machine Learning
•Linear regression assignment
•Tree Models + Boosting
•Data Modelling
•Deep learning
• Model selection & general ML techniques
*Bagging & Boosting
•CHATBOT case study