With data being at the core of the technology, data analytics has gained humongous popularity over the past few years and has become a hot topic in the market. With billions of data getting generated every day, demand for data analysts with the right skill set and knowledge will keep increasing. According to Harvard Business Review, data science is the sexiest job of the 21st century.
We have crafted a Skill Degree in Data analytics specially for professionals who want to leverage this multi billion dollar industry with dedicated mentorship from the best data folkz from our country.
Key features of SkillCircle’s Skill Degree In Data Analytics:
- 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 Analytics 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
• 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
• Working with tableau
• Data organisation
• Creation of parameters
• Advanced visualization
• Dashboard data presentation