Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. The accelerating volume of data sources, and subsequently data, has made data science to be one of the fastest growing field across every industry. Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes. A data scientist uses complex machine learning algorithms to build predictive models. The data used for analysis can come from many different sources and presented in various formats.

Data Science Objectives
1. Decision Making
Assisting businesses and organizations in making informed decisions by providing actionable insights derived from data.

2. Predictive Analysis
Using historical data to predict future outcomes. This is commonly used in finance, weather forecasting, and sales forecasting, among other areas.

3. Pattern Discovery
Identifying patterns and trends in data, which can lead to new insights or areas of interest for further investigation.

4. Optimization
Enhancing processes, resource allocation, and operations to achieve better outcomes, often through techniques like machine learning.

5. Automation
Developing algorithms that can perform tasks without explicit instructions, such as in robotic process automation or chatbots.

The lifecycle of Data Science
1. Business Understanding
The process starts with clearly defining the business goal. Without a specific problem, analysis lacks focus. Understanding the business objective ensures that the analysis aligns with the enterprise's goals, like minimizing credit loss or predicting prices.

2. Data Understanding
After setting the business objective, gather and explore the relevant data. Work with the business team to understand the data’s structure, relevance, and type. This step involves summarizing and visualizing the data to extract initial insights.

3. Data Preparation
This step involves cleaning and organizing the data. It includes handling missing values, removing inaccuracies, addressing outliers and deriving new features. Proper data preparation is essential as it directly impacts the model's accuracy.

4. Exploratory Data Analysis (EDA)
EDA involves examining the data through visualization to understand distributions and relationships between variables. This step provides insights into what influences the solution and guides the modeling process.

5. Data Modeling
Select and implement the appropriate model based on the problem type (classification, regression, clustering). Fine-tune the model’s parameters to balance performance and generalizability, ensuring it works well on new data.

6. Model Evaluation
Test the model on unseen data to ensure it meets the desired metrics. If the results are unsatisfactory, revisit and refine the modeling process until the model performs well in real-world scenarios.

7. Model Deployment
The final step is deploying the evaluated model into production. Each phase must be carefully executed, as errors in any step can compromise the entire project, from data collection to final deployment.