Online Data Science Course in PCMC Pune

Why Learning Data Science is Essential in 2024: A Long-Term Investment Challenge of Organization:

Since the start of the cloud era, businesses have moved online, generating huge amounts of data through customer interactions, business operations, and market activities. However, raw data holds little value unless transformed into actionable insights. Many companies struggle to:

• Identify trends and patterns within their data.
• Predict future outcomes to make informed decisions.
• Optimize processes for efficiency and cost savings.

Organizations create a lot of data but often lack the tools to process or understand it properly. Many companies still use old methods, which can cause mistakes and missed chances. When businesses don’t fully understand what their customers need, it can lead to dissatisfaction and lost sales. It’s also becoming harder to spot problems in data, like fraud or security risks. Data stored in different departments or systems makes it hard to bring everything together, which affects decision-making. Without tools to predict future trends, businesses struggle to prepare for changes in the market or customer needs. If data isn’t used efficiently, it can lead to higher costs and missed opportunities for improvement. Raw data often isn’t turned into useful information that helps in making important decisions. Managing data security and privacy laws is becoming a bigger challenge for many companies. There is also a shortage of skilled workers who can properly analyze and interpret data.

Solution: The Power of Data Science on above organizational challenges
Data Science has provided solutions to many of the challenges faced by organizations, making it a key role and a growing trend within businesses. As a result, companies are increasingly hiring Data Science professionals with higher salaries due to the value they bring and Data science fills this gap by utilizing advanced tools and methodologies, such as:

• Advanced Analytics: Techniques like descriptive, diagnostic, and prescriptive analytics to uncover insights and trends.
• Predictive Modeling: Using machine learning algorithms to forecast future behaviors, risks, and opportunities.
• Visualization Tools: Tools like Tableau and Power BI transform complex data into easy-to-understand dashboards for faster decision-making.

By adopting data science, companies can:

• Increase revenue through precise market targeting.
• Reduce operational costs by identifying inefficiencies.
• Improve customer satisfaction with personalized recommendations.

2. Pre-requisites:

• SQL & PL/SQL (Stored Functions, Stored Procedures, Indexes)
• Python
• Mathematics & Statistics:

3. Data Science Technologies: A Must for Data Scientists:

1. Programming Languages:
   • Python
2. Databases:
   • SQL Databases
   • NoSQL Databases
3. Mathematics & Statistics:
   • Statistics
4. Feature Engineering
5. Data Visualization
   • Matplotlib
   • Seaborn
   • Tableau
   • Power BI
6. Data Analytics Tools
   ⦁ Hadoop
7. Data Integration and Transformation Techniques
   • PySpark
8. Machine Learning & AI:
   • Building Machine Learning Models
   • Ensemble Learning
   • Clustering & Time Series Analysis
   • Artificial Intelligence
9. Deep Learning
   • Deep Neural Networks
   • Advanced Deep Learning
10. Web Application Development
    • Flask
    • Streamlit
11. Cloud Computing for Data Engineering
    • Microsoft Azure/AWS/GCP

Must Handle Mini Project Implementation in the Following Technologies

1. Project: Databases, Python (Pandas, NumPy), ML Models (supervised)
2. Project: Databases, Python, Deep learning models(Supervised)
3. Project: Databases, Python (NLTK, SpaCy), ML, DL Models
4. Project: Databases, Python (Pandas, NumPy), ML, DLModels (Ensemble)
5. Project: Databases, Python, Time Series models (ARIMA, LSTM)
6. Project: Databases, Python, ML Models (Clustering)

End-to-End Projects (At least 2)

1. Finance/Banking: Fraud Detection and Risk Management
2. E-Commerce: Customer Behavior Analysis and Recommendation Systems
3. Manufacturing: Predictive Maintenance.
4. Real Estate: Price Prediction and Market Analysis

4. Comparing Coding Complexity: Data Scientist vs Java Developers

• Data Scientist:
  1. Problem Complexity: 40%
  2. Language/Tool Complexity: 25%
  3. Domain Knowledge Complexity: 20%
  4. Experimentation & Iteration Complexity: 15%
• Java Developer:
  1. Problem Complexity: 30%
  2. Language/Tool Complexity: 35%
  3. System Architecture Complexity: 20%
  4. Debugging & Testing Complexity: 15%

5. Career Opportunities

• Machine Learning Engineer
• Data Scientist
• Data Analyst
• Business Intelligence (BI) Analyst
• Data Science Consultant
• Big Data Engineer
• Artificial Intelligence (AI) Engineer
• Data Architect

6. Eligibility Criteria:

Educational Background: A bachelor’s degree must be completed.

1. Eligible Degrees:
   • B.E. in Computer Science and similar streams
   • B.Tech in Computer Science and similar streams
   • B.C.S. (Bachelor of Computer Science)
   • B.C.A. (Bachelor of Computer Applications)
   • B.Sc. in Mathematics and Statistics (or similar relevant streams)
   • Relevant postgraduate degrees
2. Diploma Holders:
   • All streams may also be eligible.
3. Communication Skills:
   • Basic written and verbal communication skills.
   • Ability to explain technical concepts clearly.

7. Annual CTC for Data Science. (Based on Experience):

1. Entry-Level (0-1 year experience):
   • India: ₹4-6 LPA (₹4 to ₹6 Lakhs per annum)
   • USA: $60,000–$80,000
   • Job roles: Data Analyst, Junior Data Scientist, Data Engineer
2. Junior/Mid-Level (1-4 years of experience):
   • India: ₹6-14 LPA
   • USA: $80,000–$110,000
   • Job roles: Data Scientist, Business Intelligence Analyst, Machine Learning Engineer.
3. Mid-Level/Senior (4-8 years of experience):
   • India: ₹10-25 LPA
   • USA: $110,000–$140,000
   • Job roles: Senior Data Scientist, Lead Data Engineer, Data Science Manager
4. Senior/Lead (8+ years of experience):
   • India: ₹20-55+ LPA
   • USA: $140,000–$160,000+
   • Job roles: Lead Data Scientist, Data Science Director, Principal Data Scientist

Pre-requisites:

• SQL & PL/SQL (Stored Functions, Stored Procedures, Indexes)
• Python
• Mathematics & Statistics:

Data Science Technologies: A Must for Data Scientists:

1. Programming Languages:
   • Python
2. Databases:
   • SQL Databases
   • NoSQL Databases
3. Mathematics & Statistics:
   • Statistics
4. Feature Engineering
5. Data Visualization
   • Matplotlib
   • Seaborn
   • Tableau
   • Power BI
6. Data Analytics Tools
   ⦁ Hadoop
7. Data Integration and Transformation Techniques
   • PySpark
8. Machine Learning & AI:
   • Building Machine Learning Models
   • Ensemble Learning
   • Clustering & Time Series Analysis
   • Artificial Intelligence
9. Deep Learning
   • Deep Neural Networks
   • Advanced Deep Learning
10. Web Application Development
    • Flask
    • Streamlit
11. Cloud Computing for Data Engineering
    • Microsoft Azure/AWS/GCP

Must Handle Mini Project Implementation in the Following Technologies

1. Project: Databases, Python (Pandas, NumPy), ML Models (supervised)
2. Project: Databases, Python, Deep learning models(Supervised)
3. Project: Databases, Python (NLTK, SpaCy), ML, DL Models
4. Project: Databases, Python (Pandas, NumPy), ML, DLModels (Ensemble)
5. Project: Databases, Python, Time Series models (ARIMA, LSTM)
6. Project: Databases, Python, ML Models (Clustering)

End-to-End Projects (At least 2)

1. Finance/Banking: Fraud Detection and Risk Management
2. E-Commerce: Customer Behavior Analysis and Recommendation Systems
3. Manufacturing: Predictive Maintenance.
4. Real Estate: Price Prediction and Market Analysis

Training Duration: 200 Hrs

Duration: 4 Months

Mode of Data Science Training: Online

Trainer: Experienced Data Science Consultant

Choose IntelliBI Innovations & Technologies For Online data Science course in PCMC Pune

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